Culturalable and non-culturable microorganisms naturally ferment majority of global fermented foods and beverages. Traditional food fermentation represents an extremely valuable cultural heritage in most regions, and harbors a huge genetic potential of valuable but hitherto undiscovered strains. Holistic approaches for identification and complete profiling of both culturalable and non-culturable microorganisms in global fermented foods are of interest to food microbiologists. The application of culture-independent technique has thrown new light on the diversity of a number of hitherto unknown and non-cultural microorganisms in naturally fermented foods. Functional bacterial groups (“phylotypes”) may be reflected by their mRNA expression in a particular substrate and not by mere DNA-level detection. An attempt has been made to review the microbiology of some fermented foods and alcoholic beverages of the world. Traditionally, boiled rice is a staple diet with fermented and non-fermented legume (mostly soybeans) products, vegetables, pickles, fish, and meat in Far-East Asia, South Asia, North Asia, and the Indian subcontinent excluding Western and Northern India; while wheat/barley-based breads/loaves comprise a staple diet followed by milk and fermented milk products, meat, and fermented meats (sausages) in the Western and Northern part of India, West Asian continent, Europe, North America, and even in Australia and New Zealand  are widely present in many fermented foods and beverages  which is an amino acid polymer commonly present in Asian fermented soybean foods, giving the characteristic of a sticky texture to the product , and Staphylococcus (belonging to the Firmicutes) has been reported for fermented milk products, fermented sausages, meat, and fish products  is a typing method based on the genomic DNA fragment profiles amplified by PCR, and is commonly used for disintegration of LAB strains from fermented foods  and not detect a large number of predominant or diverse rare bacterial species identified in pyrosequencing analysis  and temperature gradient gel electrophoresis (TGGE) have been developed to profile microbial communities directly from fermented foods, and are based on sequence-specific distinctions of 16S rDNA and 26S rDNA amplicons produced by PCR  technique using restriction enzymes is also useful in identification of microorganisms from fermented foods , using housekeeping genes as molecular markers alternative to the 16S rRNA genes, is used for LAB species identification: kimchi, a naturally fermented vegetable product of Korea  such as metagenomics, phylobiomics, and metatranscriptomics are nowadays applied for documentation of cultures in traditionally fermented products  has been used to identify species of Campbell-Platt  reportedkefir, koumiss), and moldy milks  into alcoholic beverages or by using food beverages Tamang, , whereasEnterococcus, Lactococcus, Lactobacillus, Leuconostoc, Pediococcus, Streptococcus, and Weissella are common bacteria associated with cereal fermentations  with the stickiness characteristic, and soybean foods fermented by filamentous molds, mostly Aspergillus, Mucor, Rhizopus , east Nepal and north-east India (kinema and its similar products), and northern Thailand (thua-nao), named as “natto triangle” -triangle”  root is traditionally fermented into staple foods such as gari in Nigeria; fufu in Togo, Burkina Faso, Benin and Nigeria; agbelima in Ghana; chikawgue in Zaire; kivunde in Tanzania; kocho in Ethiopia; and foo foo in Nigeria, Benin, Togo, and Ghana, respectively , Pichia membranifaciens, Sacch. Cerevisiae, and Kluyveromyces species for flavor development  with rice or wheat as the base in the form of dry, flattened or round balls, for production of alcoholic beverages is a remarkable discovery in the food history of Asian people, which is exclusively practiced in South-East Asia including the Himalayan regions of India, Nepal, Bhutan, and China  fibuligera, Sm. capsularis, Pichia anomala, Pic. burtonii, and Candida glabrata; ; filamentous molds ; LAB ; amylase-producing bacilli ; and acetic acid bacteria  is present in men, a starter culture of Vietnam  also produces amylases that convert starch to fermentable sugars, which are then used for the second stage yeast fermentation to make non-alcoholic fermented soybean miso and shoyu  fermentation e.g., beer  e.g., sorghum (“Bantu”) beer of South Africa  with 5% alcohol content , the proportion of the Saccharomycetaceae family increases significantly and the major bacterial phylum of the samples shifts from γ-Proteobacteria to Firmicutes  are not distilled but the extract of fermented cereals is filtered into clarified high alcohol-content liquor, like in sake, which is a national drink of Japan containing 15–20% alcohol  drink of the Himalayas with aromatic characteristic, and distilled from the traditionally fermented cereal beverages  beverage of Andes Indian race of South America mostly in Peru, prepared from maize by human salivation process  beverage  plant of Mexico , the γ-Proteobacteria , Zymomonas mobilis, Acetobacter malorum, A. pomorium, Microbacterium arborescens, Flavobacterium johnsoniae, Gluconobacter oxydans, and Hafnia alvei  and non-Saccharomyces , Micrococcus sp., Pediococcus sp., Bacillus sp., and Sarcina sp.  is malted  yeasts  provides free of charge access or links to comprehensive datasets comprising genomic, transcriptomic, proteomic and metabolomic information (Pretorius et al., The most common example of alcoholic beverages produced from fruits without distillation is wine, which is initiated by the growth of various species of Archaea has been ascribed roles in some fermented foods and alcoholic beverages. However, consumption of lesser known and uncommon ethnic fermented foods is declining due to the change in lifestyles that is shifting from cultural food habits to commercial foodstuffs and fast foods, drastically affecting traditional culinary practices, and also due to the climate change in some environments such as the Sahel region in Africa and the vast areas adjacent to the Gobi desert in Asia.Every community in the world has distinct food culture including fermented foods and alcoholic beverages, symbolizing the heritage and socio-cultural aspects of the ethnicity. The word “culture” denotes food habits of ethnicity; another meaning for the same word “culture” is a cluster of microbial cells or inoculum, an essential biota for fermentation, often used in the microbiology. The diversity of functional microorganisms ranges from filamentous molds to enzyme-producing and alcohol-producing yeasts, and from Gram-positive to a few Gram-negative bacteria, while even JT: contributed 50% of review works. WH, contributed 25% of review. KW contributed 25% of review.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mental imagery research has weathered both disbelief of the phenomenon and inherent methodological limitations. Here we review recent behavioral, brain imaging, and clinical research that has reshaped our understanding of mental imagery. Research supports the claim that visual mental imagery is a depictive internal representation that functions like a weak form of perception. Brain imaging work has demonstrated that neural representations of mental and perceptual images resemble one another as early as the primary visual cortex (V1). Activity patterns in V1 encode mental images and perceptual images via a common set of low-level depictive visual features. Recent translational and clinical research reveals the pivotal role that imagery plays in many mental disorders and suggests how clinicians can utilize imagery in treatment. Recent research suggests that visual mental imagery functions as if it were a weak form of perception.Evidence suggests overlap between visual imagery and visual working memory – those with strong imagery tend to utilize it for mnemonic performance.Brain imaging work suggests that representations of perceived stimuli and mental images resemble one another as early as V1.Imagery plays a pivotal role in many mental disorders and clinicians can utilize imagery to treat such disorders. Mental imagery has played a central role in discussions of mental function for thousands of years. Many have argued that it is one of the primary human mental events that allow us to remember, plan for the future, navigate, and make decisions. In addition, mental imagery plays a core role in many mental health disorders and plays an increasingly important role in their treatment.We use the term ‘mental imagery’ to refer to representations and the accompanying experience of sensory information without a direct external stimulus. Such representations are recalled from memory and lead one to re-experience a version of the original stimulus or some novel combination of stimuli. Note that not all mental imagery need be voluntary; external events or internal associations also can trigger a mental image, even if one does not want to experience the image at that time Historically, mental imagery research suffered for both practical and theoretical reasons. Methodological constraints caused by imagery's inherently private nature put practical limits on the types of mechanistic investigation that could be performed. Furthermore, the second half of the 20th century saw the rise of behaviorism in psychology. This theoretical orientation rejected the study of internal representations, including mental imagery. The combination of these two impediments is largely responsible for the comparative lack of mental imagery research relative to related topics such as visual attention and visual working memory Such constraints are now lifting, with increasingly sophisticated research techniques leading to many new discoveries about imagery. In recent years, new objective research methods have permitted more direct investigations into the mechanisms and neural substrates of mental imagery. Results from these methods shed light on mental imagery's role in perception, cognition, and mental health. Findings have cemented our understanding of visual mental imagery as a depictive internal representation with strong and unexpected ties to visual perception, effectively ending the so-called ‘imagery debate’ Much of the work on imagery and perception in the 1990s and 2000s revealed that imagery shares processing mechanisms with like-modality perception. For example, researchers showed that imagined visual patterns interact with a concurrent perceptual stimulus to boost sensory performance in a detection task Mental images can also take the place of perceptual stimuli during various types of learning. Perceptual learning typically involves repeated performance of a perceptual detection or discrimination task that leads to increases in performance. However, imagining the crucial components of such a task, instead of actually performing them on a perceptual stimulus, can also enhance performance on the perceptual task binocular rivalry’ technique  – was first applied to mental imagery in 2006 retinotopic location  and then used it to decode mental images of high-contrast blobs in various configurations. Consistent with the evidence for retinotopic organization in mental imagery In addition, brain imaging work has provided compelling evidence that visual mental images arise from activation of the same types of visual features that are activated during visual perception. Several studies have explicitly modeled the representations encoded in activity during perception and then used the model to decode mental images from brain activity. To our knowledge this explicit modeling approach – known as low-level visual features is conserved even when people visualize complex, multi-object scenes such as photographs and artwork It could be argued that imagining simple, blob-like stimuli that are optimized to engage area V1 is a special case of mental imagery that may be unrelated to the rich, complex imagery we generate and use daily. However, the representation of Furthermore, sensitivity to both perceptual orientation and location in visual space has been linked to the anatomy of V1 Together, these studies show that activity patterns in the visual cortex are not merely similar across visual mental imagery and perception: activity patterns encode a common set of visual representations. When considered in light of the behavioral evidence reviewed above, these results lend further support to the conceptualization of visual mental imagery as a weak or noisy form of top-down perception that can in some cases take the place of bottom-up perception.Although mental imagery and visual working memory both involve the ability to represent and manipulate visual information, research on the two topics has diverged into two separate literatures that rarely reference one another When participants in visual working memory experiments are asked to describe the strategies they use to complete the memory task, they tend to describe one of two different strategies. One involves creating a mental image to compare with the subsequent test stimuli Recent behavioral work supports these subjective reports of different strategies Taken together, these behavioral data suggest that those with relatively strong mental imagery utilize it to perform a visual working memory task whereas those with weaker imagery tend to rely on nonvisual strategies.Brain imaging work has demonstrated overlap in the neural representation of visual working memory and mental imagery. For example, in one study The combination of behavioral and brain imaging data shows that, despite clear task differences , mental imagery and visual working memory can share common neural mechanisms in the sensory cortex. In many tasks, participants have to decide for themselves how best to maximize their memory performance. Depending on the ‘mental tools’ at hand, this might be with mental imagery or a propositional strategy. Recent work suggests that imagery strength and the neural networks underlying imagery may play a role in how individuals perform such tasks The key to unlocking the mechanistic relationship between visual imagery and visual working memory may lie in the individual differences across the population in visual representational strength, physiology, and even anatomy The human visual system is a constellation of functionally distinct areas. These areas are conceived of as being organized in a hierarchy. Activation in areas toward the top of the hierarchy – so-called ‘high-level’ visual areas – is sensitive to changes in the semantic content of visual scenery and is invariant to visual detail. These areas are located in the ventral temporal lobe and representations encoded in the activity of these areas become increasingly abstract toward the anterior pole. Areas toward the bottom of the hierarchy – the ‘early’ visual areas – are located in the occipital cortex and are exquisitely sensitive to visual detail .Given this organization and the fact that early visual areas both send projections to and receive projections from high-level visual areas, many researchers have predicted that the role of the early visual areas in mental imagery is to flesh out visual detail.Between 1993 and 2010, at least 20 studies attempted to test this hypothesis by using brain scanning to compare the amplitude of activity in early visual areas during visual mental imagery with the amplitude of activity during perception (or rest). Many studies reported no significant activity above baseline in the early visual cortex during mental imagery multivariate pattern classifiers (MVPCs) have shown that the same pattern classifiers that accurately discriminate stimuli by analyzing patterns of activity in visual areas V1 and V2 during perception of simple external stimuli can also discriminate the same stimuli during mental imagery However, activation is only part of the picture. Recent studies using MVPC studies have also revealed similarity in activity patterns across visual perception and imagery in high-level visual areas Taken together, the MVPC and activation studies indicate that activity patterns associated with matched external and imagined stimuli begin to resemble one another as early as area V1. The resemblance increases with ascension of the visual hierarchy, although vividness of imagery appears to be most closely associated with early visual areas.This general picture of how mental imagery engages the visual cortex satisfies many intuitions about how mental images are generated. For example, mental imagery is presumably based on the recall and recombination of memories. Because high-level areas are physically  closer to memory-encoding structures in the medial temporal lobe than are earlier visual areas, it makes sense that the activity patterns associated with perceived and imagined images should more closely resemble one another in high-level than in early visual areas. This may also explain why the semantic aspects of mental images tend to be less ambiguous than visual details . Lastly, it makes sense that the parts of the visual system responsible for visual detail should be most closely coupled to the visual vividness of mental images.In a similar time frame to the burgeoning fundamental mechanistic investigations we have discussed so far, mental imagery has also been found to play a pivotal role in many mental and neurological disorders and their treatments. For example, intrusive, emotional mental imagery causes distress across a range of psychiatric disorders, from post-traumatic stress disorder (PTSD) and other anxiety disorders to bipolar disorder and schizophrenia After a psychologically traumatic event, a significant proportion of people develop PTSD Recent years have witnessed an explosion of research suggesting that mental imagery plays a role across a wide range of mental disorders Given the centrality of intrusive emotional imagery in such a wide variety of mental disorders, a basic understanding of mental imagery could prove instrumental in the development of new treatments. Potentially critical issues include the relative emotional impact of different representational formats, imagery ‘realness’, and the perceived likelihood of imagined events occurring.Until recently, surprisingly little research had tested the relative impact of picture-like formats versus language-like descriptive formats on emotion; that is, mental imagery versus verbal thought  often includes ‘imaginal exposure’, which involves having the patient repeatedly imagine the feared object or context  until his or her anxiety level subsides Another technique, ‘imagery rescripting’ These imagery-focused therapeutic techniques reduce the powerful impact of dysfunctional imagery on emotion and/or reduce the frequency of associated intrusive imagery. It is noteworthy that imagery-focused CBT, as reviewed in clinical guidelines How might mental imagery research lead to future treatment innovations? First, we can import existing imagery techniques to clinical areas where imagery has been neglected. For example, treatments for bipolar disorder have shown little improvement since the discovery of lithium many decades ago. Perhaps advances can be made by leveraging the fact that people with bipolar disorder show high spontaneous use of imagery and intrusive imagery Second, basic science studies of mental imagery may inform the development of new imagery treatment techniques by focusing on the depictive, pictorial format of imagery itself. For example, as discussed above, concurrent perception may interfere with image generation Overall, discoveries about mental imagery can contribute to our understanding of the cognitive and neural mechanisms underlying psychopathology and of which mechanisms to target in improving treatments The many new methods touched on here not only offer new mechanistic insights into mental imagery but also offer new tools for future research. Recent work has demonstrated how imagery can ‘stand in’ for an afferent visual representation of an external stimulus. Specifically, mental images seem to behave much like weak versions of externally trigged perceptual representations. Functional brain imaging work supports the behavioral evidence by demonstrating that common sets of neural structures are employed during both events. Further, both representations seem to be encoded using a common set of basic visual features, which in many visual areas are organized topographically.An increasingly important component of imagery research now and in the future is the translation of the fundamental science into the clinic. Clinical research shows that many different mental disorders involve symptomatic imagery, and incorporating imagery into behavioral treatments is proving beneficial. Bridges from fundamental research to emotional imagery will be critical for the systemic understanding of mental representations in dysfunction. Similarly, the characteristics and function of mental representations in everyday cognition will help form a fuller understanding of human mental events.Outstanding QuestionsHow do perception and mental imagery differ? There are clear phenomenological and epistemological differences between external perceptual and mental images, and patterns of activity measured during imagery and perception of the same stimulus are not identical. A first step toward answering this question will be to discover whether imagery-induced neural activity patterns are simply weaker or noisy versions of the activity during the perception of matched external stimuli or whether they encode systematically distorted representations.Are individuals able to exploit the differences between mental imagery and perception?How does mental imagery differ from other forms of top-down activity? Visual perception is heavily influenced by working memory, attention, and expectation. Clearly mental imagery is related to these disparate cognitive phenomena, but more work is needed to elucidate the networks and patterns of neural activity that distinguish mental imagery from these and other modes of cognition and perception.Can mental imagery be involuntary, as clinical theory proposes? Or do individuals simply lack conscious awareness of the voluntary process ? The type of imagery prevalent in many mental disorders is typically described as involuntary, or not under the individual's control . Little is known about the mechanisms that distinguish voluntary and involuntary imagery.Can mental images be generated non-consciously?What functional mechanisms dictate individual differences in imagery strength?There are many examples of visual illusions that create a conscious visual experience without a direct stimulus. Might the involuntary nature of such phantom perceptual experience offer a novel way to study the involuntary elements of imagery?The main functions of mental imagery include simulating possible future scenarios and ‘reliving’ past experiences The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the United Kingdom Medical Research Council.


Euptelea species in China (E. pleiosperma) and Japan (E. polyandra). Chloroplast/nuclear DNA sequences and microsatellite loci were obtained from 36 Euptelea populations to explore molecular structure and diversity in relation to past and present distributions based on ecological niche modelling (ENM). Time-calibrated phylogenetic/phylogeographic inferences and niche-identity tests were used to infer the historical process of lineage formation.The evolutionary origin and historical demography of extant Arcto-Tertiary forest species in East Asia is still poorly understood. Here, we reconstructed the evolutionary and population demographic history of the two extant Euptelea pleiosperma diverged from E. polyandra around the Late Miocene and experienced significant ecological differentiation. A near-simultaneous diversification of six phylogroups occurred during the mid-to-late Pliocene, in response to the abrupt uplift of the eastern Tibetan Plateau and an increasingly cooler and drier climate. Populations of E. pleiosperma seem to have been mostly stationary through the last glacial cycles, while those of E. polyandra reflect more recent climate-induced cycles of range contraction and expansion.Our results illustrate how Late Neogene climatic/tectonic changes promoted speciation and lineage diversification in East Asia’s Tertiary relict flora. They also demonstrate for the first time a greater variation in such species’ responses to glacial cycles in Japan when compared to congeners in China.The online version of this article (doi:10.1186/s12862-016-0636-x) contains supplementary material, which is available to authorized users. We compiled 19 environmental variables for each period from the WORLDCLIM database with a resolution of 2.5 arc minutes [E. pleiosperma and 371 records for E. polyandra, a current distribution model was developed for each species using five bioclimatic data layers  at 2.5 arc-minutes resolution for the present (1950–2000). This limited bioclimatic dataset avoided containing highly correlated variables (data not shown), and therefore prevented potential overfitting [community climate system model v.3.0 [http://pmip2.lsce.ipsl.fr/). Following Sakaguchi et al. [c. -130 m lower than at present) [ENMs were generated using the maximum entropy method implemented in  v.3.2.1  to predi minutes  for eachrfitting . By adoprfitting . We thenel v.3.0 , providei et al. , the prepresent) , we usedE. pleiosperma and E. polyandra .To examine potential environmental factors associated with the divergence between  v.3.1.1 . We also v.3.1.1  in enmto v.1.4.3  with 500 v.1.4.3 . Niche o1.4.3 [D  and the Euptelea was 2078 bp in length, including 25 single-nucleotide variable sites, five indels (5–18 bp), and one inversion  and 10 to E. polyandra , and this variation exhibited significant phylogeographic structure . In the tcs network  were specific to a group of six populations  from the region bordering the eastern QTP/Hengduan Mts.  identified segregated populations into the southern and central parts of Japan  and E. polyandra (R6–10). The parsimony network , separated by nine mutational steps, resulting in 80.08 % among-species variation  was also the most frequent and widespread one, being fixed in 14 (out of 26) populations, while in E. polyandra almost all populations (8/10) were polymorphic. In contrast to cpDNA, most of the within-species ITS variation resided within populations  corresponding to K = 3 .Across the 440 individuals of r I Fig.  and b thter Fig.  and b. ARCT = 0.23, P < 0.01). Within each species, most of this variation resided within populations, with somewhat stronger population subdivision in E. pleiosperma  than in E. polyandra .Hierarchical AMOVA Table  supportebeast-derived cpDNA + 26S nrDNA chronogram of Ranunculales  and estimated the crown age of Euptelea as c. 5.46 Ma [95 % highest posterior density (HPD): 1.23–10.87 Ma; see node A in Fig. Euptelea  of all 35 chlorotypes was estimated as c. 6.04 Ma  was estimated to be only slightly younger, c. 3.20 Ma  Ma; C2: 1.66 (0.42–3.50) Ma; C3: 1.15 (0.17–2.68) Ma; C4: 1.73 (0.48–3.47) Ma; J1: 1.50 (0.36–3.07) Ma; J2: 2.31 (0.88–4.13); see nodes C1–4 and J1–2 in Fig. The les Fig.  recoverelea Fig. , the timE. pleiosperma, the Bayesian skyline plots analysis , followed by stable population sizes until the present, while for each of the four phylogroups,Ne kept steady through time (data not shown). By contrast, for E. polyandra and its two phylogroups, this analysis consistently indicated stable population sizes up until the Late Pleistocene (c. 0.5 Ma), followed by a slight growth until the present , indicating a good predictive model performance. The predicted distribution of Euptelea under current conditions (1950–2000)  or Hokkaido/north Honshu of Japan and central/east China (E. polyandra). During the LIG  and the surroundings of the Sichuan Basin. By contrast, at the LGM  and along the coasts of the exposed East China Sea continental shelf; however, for each species, hospitable habitats were apparently absent on the landbridge extending from east China across south Korea to south Japan  for the distribution modelling, averaged over 50 replicates, were very high (0.958 ± 0.016 vs 0.971 ± 0.002 for 00) Fig.  was simipan Fig. .Fig. 5PoE. pleiosperma and E. polyandra  . Within E. pleiosperma, seven (out of 19) bioclimatic variables showed significant (P < 0.05) differences between the western and central-eastern cpDNA lineages, i.e. in terms of isothermality (bio3), temperature/precipitation seasonality (bio4/bio15), and aspects of temperature  in either instance , showed significant differences in mean values between 01) Fig. , regardlEuptelea and provide the first evidence for the genetic distinctiveness of E. pleiosperma (China) and E. polyandra (Japan). In addition, although these ecological differences  data Figs. , 3 and 4c. 6.04  Ma. Notably, this point estimate broadly coincides with an early stage of East China Sea seafloor exposure between the Eurasian mainland and the Japanese/Ryukyu Islands (7.0–5.0 Ma) [c. 7–5.3 Ma) [E. polyandra and in southeast Japan for E. pleiosperma, but no such areas were predicted for either species on the ECS landbridge  [Based on our cpDNA chronogram Fig. , this sp–5.0 Ma) , 92. Hen–5.3 Ma) , 94 and/dge Fig. . Therefo.015 Ma) , 92, 95.Kirengeshoma palmata; Platycrater arguta; Ligularia hodgsonii) [Cercidiphyllum japonicum [Kalopanax septemlobus [Euptelea spp., C. japonicum is also an Arcto-Tertiary relict of riparian plant communities with similar reproductive characteristics  [C. japonicum, which is presently found up to north Japan, is relatively tolerant to cold and arid climates, which might have facilitated gene exchange across the glacially exposed ECS landbridge up until its latest submergence  properties [There is increasing evidence to suggest that the East China Sea landbridge acted as a ‘filter’ for East Asia’s temperate flora in selectively preventing or facilitating the dispersal of certain plant species , 96. Fordgsonii) , 96–98. aponicum  and Kalotemlobus . As withagation) , 99, 100 ability . We therE. pleiosperma  and E. polyandra  occurred at the mid-to-late Pliocene. These broad HPD ranges preclude us from explaining any of these splits in terms of particular geological or palaeoclimatic events. Despite these caveats, we propose that our dates are still broadly consistent with the mid-Pliocene abrupt uplift of the eastern Tibetan Plateau and adjacent southwest China (c. 3.4 Ma) [E. pleiosperma  and two phylogroups of E. polyandra (J1 vs J2). In fact, such Late Neogene tectonic/climate-induced vicariance has also been invoked to explain phylogeographic splits in other Tertiary-relic deciduous tree species from East Asia  [E. pleiosperma  and E. polyandra  are generally more recent  , and the 3.4 Ma) . These gcuneata) , 104. Hoent Fig. ; Table 2Cercidiphyllum japonicum [Sargentodoxa cuneata [E. pleiosperma in this region. This inference is based on three lines of evidence. First, E. pleiosperma exhibits strong phylogeographic structure in cpDNA , which largely reflects the presence of four montane phylogroups, one in the west (C1), and three (C2–4) in the Central-East , followed by population stability until the present. Finally, our ENM for the LGM  gene pool (II), excepting three from the SB region  that share gene pool I with most western populations  and the other in central Japan (north Honshu)  likely triggered ecological changes [E. polyandra. Accordingly, this phylogeographic break in central Honshu, which previously has also been observed in several other biota [E. polyandra (and its phylogroups) experienced long-term demographic stability before Ne started to increase only relatively recently, c. 0.5 Ma  [c. 1.0–0.78 Ma) [E. polyandra [E. polyandra experienced a dramatic contraction of its (present-like) LIG range into a narrow coastal strip along the Pacific Ocean side of southeast Japan  compared with areas further south  Fig. , b. Give changes , and hener biota , 116–1180.33 Ma)  and the 0.78 Ma) . For moiolyandra , this mapan Fig.  and b. Ipes Fig.  and b, aNA Figs.  and 3. IEuptelea reflect the primary influence of successive climate/tectonic-induced vicariance during the Late Miocene and Pliocene periods. By contrast, the haplotype diversity and structure at the within-group level of each Euptelea species are most likely shaped by the climatic cycles of the (Late) Quaternary. During this time, E. pleiosperma populations seem to have been mostly stationary within their multiple mountain refugia, whereby topographical constraints to seed/pollen dispersal fostered the divergence among ‘western’ and ‘central-eastern’ cpDNA phylogroups. Contemporaneously, E. polyandra populations in south Japan apparently underwent repeated range contractions/expansions, while those in central Japan may have persisted in a cryptic glacial refuge in north Honshu. Overall, our results (1) illustrate how Late Neogene climatic changes promoted speciation and lineage diversification in East Asia’s Tertiary relict flora; and (2) demonstrate for the first time a greater variation in such species’ responses to glacial cycles in Japan when compared to congeners in China. Overall, this study should contribute to a better understanding of the potential historical  processes that may have generally impacted the East Asia’s Arcto-Tertiary flora.The speciation and lineage diversification events of E. pleiosperma and E. polyandra in Additional file http://www.treebase.org/) under the submission number 18919 (URL: http://purl.org/phylo/treebase/phylows/study/TB2:S18919).DNA sequences: see Genbank accessions for


This is the first record of a population of box jellyfish closely associated with the benthos at such depths. Chironex were not widespread, occurring only in 2 of 33 tow videos and 3 of 41 epibenthic sleds spread over 2000 km2. All Chironex filmed or captured were on low to medium relief reefs with rich filter feeder communities. None were on soft sediment habitat despite these habitats comprising 49% of all sites. The importance of the reef habitat to Chironex remains unclear. Being associated with filter feeder communities might represent a hazard, and other studies have shown C. fleckeri avoid habitats which represent a risk of entanglement of their tentacles. Most of our observations were made during the period of lowest tidal current flow in the morning. This may represent a period favourable for active hunting for prey close to the seabed.Box jellyfish cause human fatalities and have a life cycle and habit associated with shallow waters (<5 m) in mangrove creeks, coastal beaches, embayments. In north-western Australia, tow video and epibenthic sled surveys discovered large numbers (64 in a 1500 m tow or 0.05 m Chironex and have been responsible for numerous fatalities in tropical and subtropical coastal regions of the world. The most notorious of these is Chironex fleckeri, which has been responsible for 77 deaths in Australia alone1C. yamaguchii has been responsible for fatalities in Japan, and probably the Philippines3Chironex remain a mystery, and this is true for cubozoan jellyfish in generalChironex have been made in shallow coastal waters where they are a hazard for swimmers, and where known, their sessile polyp stage occurs in mangrove creeks and the medusa stage enables them to spread among shallow coastal areas6Chironex in deeper shelf waters have hitherto been limited to reports of catches by prawn trawlersC. fleckeri away from the mainland or large continental islands or in depths >5 m. Earlier 47 offshore surveys over 2 years taking 1200 surface and subsurface plankton samples (4000 cubic meters) captured only 8 C. fleckeriAlatina alata was recorded to 675 mCubozoans or box jellyfish include members of the highly venomous genus Chironex on deep coastal reefs arose during work for a large scale survey using an underwater towed video camera and epibenthic sled to characterise the sea bed habitats and biodiversity of Camden Sound, a remote, previously unsurveyed location in the Kimberley region of north-western Australia. During the survey of March 2015, one of the routine video stations discovered large numbers of box jellyfish close to the seabed in over 40 m of water. Epi-benthic sleds carried out at stations nearby also caught box jellyfish. This paper is the first to report on large numbers of box jellyfish close to the benthos on deep reefs (40+ m) with rich filter feeder communities, and our objective is to document this phenomenon as a contribution to the scant information on box jellyfish habitat.The opportunity to observe Chironex fleckeri. There is reported to be a species of Chironex from the Kimberley region of Australia that remains undescribedChironex fleckeri and have been lodged with the Western Australian Museum, registration numbers, WAM Z68783  in each one minute segment of the 40 minute video and eight of the 64 were observed as four pairs (counted in same frame). Box jellyfish were caught in just three sleds (6.8%), at station 8 (48 m deep), Station 100 (39 m) and Station 120 (53 m), catching 4, 7 and 1 individuals respectively  and 6.14 to 6.25 mg Oxygen per litre (see Camden Sound comprises a mix of benthic habitats. At twenty three towed video stations (70%) we recorded low diversity, soft sediment, usually mud habitats. At the other ten towed video transects (30%) we recorded diverse filter feeder  communities. Both video transects on which the box jelly fish occurred and all three of the sled stations which caught samples were also in diverse filter feeder habitats see . In Noveitre see .Chironex fleckeri67o S, 124.196o E), but from deeper water (39–56 m), 28 km offshore from the nearest mainland and creek system. The significance of the observations of Chironex right at the benthos on a diverse filter feeder dominated community low to medium relief reefs and the importance of this type of habitat to them remains unclear. We did not capture or observe any Chironex specimens on soft sediment habitats which are structurally less complex and less biodiverse, despite these habitats making up 49% of all 74 sites sampled (70% of videos and 32% of sleds). HartwickChironex made highly complex three dimensional habitats such as mangrove creeks unsuitable. The same could be said for filter feeder reefs where we made our observations. Unlike most jellyfish, box jellyfish are strong swimmersChironex fleckeri usually cease swimming and orient the dorsal section of the bell downwards7Chironex fleckeri feeds amongst mangroves from November to March in Darwin (Northern Territory), but moves out of the trees as soon as the slack water changes to tidal flow. This behaviour is likely to be to avoid entanglement. Another chirodropid, Chirodectes maculatus15The habit, habitat and early life history of Chironex fleckeri prey predominantly on prawnsChironex we caught in sleds or observed on the video were between 0800 h and 0930 h and none had obvious prey items being digested. A possible explanation for our observations is that the Chironex seek shelter from strong currents on reefs by day, where they could also hunt fish, and hunt for nocturnally emergent prawns on adjacent soft sediment habitats at night. One conflict for this explanation is that our survey was timed to coincide with neap tides and the video tow 149 on which we observed most jellyfish was during the slackest part of the tide on that day. Nevertheless currents in this area are strong at all times, but it is possible that our observation of large numbers of jellyfish on these reefs at these depths might also be explained by it being at the period of lowest tidal current. This period of lowest water movement may represent a favourable time for active hunting. Some carybdeid jellyfish are known to hunt near the seabed by swimming oblique transects along the edges of vegetative patches, where the light-dark contrast is strongestChironex in March 2015 but not November 2014 suggest their occurrence there is seasonal or episodic and is consistent with the known seasonality of the peak abundance of large medusa in March and AprilChironex are known from the west coast of the Australian continent, there have been records of human envenomationsChironex in this region and more ecological research on this species is needed. In addition resolving the taxonomy and distribution of this species is important; given some studies attribute the Chironex in north-western Australia as being C. fleckeri10Chironex species as we captured at the benthic sled stations nearby.The study area was in Camden Sound located in the central Kimberley region of Western Australia  where a Chironex sp were captured were 8 , 100  and 120 (15 March 1500 h). The density of jellyfish was calculated in four ways; per linear m of video tow, per m2 of video tow (using 0.84 m camera port view width × tow length), per m2 of seabed sampled by the sled and per m3 of water sampled by the sled (sled mouth area 0.495 m2 × tow length). A retrospective viewing was also made of videos taken in November 2014 along the same track as the three epibenthic sleds which caught box jellyfish in March 2015. Depth profiles of temperature (oC), salinity (PSU) and oxygen (mg/litre) were made with a Sea-BirdTM SBE19+ CTD profiler  at three stations in the area to determine the extent of any stratification of the water column r column .How to cite this article: Keesing, J. K. et al. Abundant box jellyfish, Chironex sp. (Cnidaria: Cubozoa: Chirodropidae), discovered at depths of over 50 m on western Australian coastal reefs. Sci. Rep.6, 22290; doi: 10.1038/srep22290 (2016).


While considerable research has examined the variation of vegetation distribution within cities and related social and economic drivers, we know very little about whether or how the economic characteristics and policy priorities of green cities differ from those of “grey” cities—those with little green land cover. To address this gap, this paper seeks to answer the question how do the economic characteristics and policy priorities of green and grey cities differ in the United States? To answer this question, MODIS data from 2001 to 2006 are used to characterize 373 US cities in terms of their vegetative greenness. Information from the International City/County Management Association's (ICMA) 2010 Local Government Sustainability Survey and 2009 Economic Development Survey are used to identify key governance strategies and policies that may differentiate green from grey cities. Two approaches for data analysis—ANOVA and decision tree analysis—are used to identify the most important characteristics for separating each category of city. The results indicate that grey cities tend to place a high priority on economic initiatives, while green cities place an emphasis on social justice, land conservation, and quality of life initiatives.In the United States, urbanization processes have resulted in a large variety—or “continuum”—of urban landscapes. One entry point for understanding the variety of landscape characteristics associated with different forms of urbanization is through a characterization of vegetative (green) land covers. Green land covers— Vegetntenance –10) and iversity ,12) of viversity –16. Thisiversity –19 to reiversity –25.Variation in vegetative land cover has been shown to be associated with a wide range of human and environmental outcomes, from the extent of urban heat islands , to sociUnderstanding the mechanisms that result in vegetated landscapes is important, as vegetative greenness in cities has been shown to influence valued environmental and human outcomes both positively and negatively. For example, accessible green space—including land cover types such as lawns—has been shown to improve the formation of strong social ties among neighbors ,30, reduHowever, increasing vegetative cover in urban areas comes with political and economic challenges for program management and maintenance . A signibetween cities. While the geographic location of a city clearly influences the availability of water and other inputs necessary to foster plant growth, the highly modified nature of urban environments means that geography is unlikely to entirely explain the variation in vegetation between cities . ,50. betwc.f. [First, green cities may have different priorities, expressed through general policy initiatives, than grey cities. For example, highly vegetated cities may be more likely to prioritize environmental outcomes, quality of life goals, social justice, green space and green infrastructure; grey cities may be more likely to prioritize economic growth and development (though policy tradeoffs may complicate these relationships) c.f. ,52). Sec. Secc.f.c.f. ,54. Thirc.f. . Finallyc.f. ,56. In tA total of 373 US cities are examined in this paper using two types of data: survey information and satellite observed vegetative greenness information (NDVI). These cities, a convenience sample based on responses to two International City/County Management Association's (ICMA) surveys, range in population size from 1,068 to 104,590 (2005 Census) and are located in nearly all 48 contiguous US states see .In order to examine the political and economic characteristics of green and grey cities, responses to the ICMA's surveys on sustainability and economic development were used to characterize the cities along the four dimensions, or themes, described previously as potentially being related to the greenness of a city: (1) general policy initiatives; (2) land use/cover policies; (3) economic base; and (4) perceived economic barriers. The questions used for each sub-topic, as well as the variable name assigned to each question, are summarized in i.e., fine-scale vegetation patch spatial structures—within urban environments  and maximum vegetative index values across each time step. These images are then classified using a Normalized Difference Vegetation Index (NDVI). Values approaching 0 indicate very sparse vegetation, and dense vegetation is indicated by values approaching 1. To estimate the density of vegetation across each of our 373 cities, the average NDVI value from 2001 to 2006 was retrieved within the US Census Place geographic boundaries associated with each city. Cities with above-average NDVI values were categorized as “Green” cities, while below-average cities were categorized as “Grey”.To facilitate the NDVI classification, satellite information was retrieved from the Global Land Cover Facilities US Vegetation Index product, which is derived from daily MODIS 250 m resolution red and near infrared bands . ComposiEach step of the data collection and analysis process is summarized in via the metric G2)—i.e., the algorithm attempts to correctly classify the largest number of cities within each split. A lower G2 value indicates a better “fit” at a given node within the tree. Based on the position of a splitting variable within the tree the relative importance of variables in defining a cities category (below or above average vegetative greenness) can be identified.Decision tree classification techniques have become increasingly popular in remote sensing research for their ability to provide nonparametric “data mining” approaches to classifying satellite imagery . In our Descriptive results for the economic characteristics and policy priorities of the cities can be found in Vegetative density as measured using MODIS using NDVI ranged from 0.296 to 0.832, with a mean of 0.61 (σ = 0.114). The distribution is slightly right-skewed . FurtherF statistics indicate a statistically significant difference between groups, and within-group means can be examined to assess the directionality associated with having a given policy or economic focus. In our analysis, four variables were significant in their relationship to vegetative greenness : having a general economic (−) and/or social justice (+) governance focus, having land conservation policies in place (+), and reporting land as an economic barrier to development (+).The results from the ANOVA can be seen in G2− value in the two nodes it splits into suggests that the variable does not have strong explanatory capabilities on its own. To improve model fit, cities that do not have a tourism-based economy are further differentiated according to whether or not they have strong economic policy initiatives. Cities with strong economic policy initiatives are differentiated by whether they also have strong quality of life policy initiatives, and those cities without quality of life policy initiatives have below average vegetation cover. Among cities that do have strong quality of life policy initiatives, those that have an institutional economic base  have higher vegetation levels than average.The results from the decision tree analysis can be seen in Cities without strong economic policy initiatives are differentiated first by whether or not they also have a strong trails policy initiative and those cities without have lower vegetation levels than average. Cities that do have strong trails policy initiatives are first differentiated by whether they also have strong social justice policy initiatives. Cities that do not have a strong social justice policy initiative and also identify land availability as a barrier to economic development have higher vegetation levels than average. Cities that do have strong social justice policy initiatives and also have conservation-based land use policies have higher vegetation levels than average.2 = 0.3), and used similar variables  to differentiate grey and green cities. The geographically-stratified model did not identify the lack of trail policy initiatives, land economic barriers, and quality of life initiatives but adding an additional 3 splits  to the model reintroduces these variables, suggesting that model differences are largely due to the additional complexity introduced into the model by the geographic regions. Small differences between Mid-Plains cities and other cities emerged in terms of the relative importance of variables. Two key examples are that tourism economic base was more important in the Mid-Plains, while quality of life initiatives were more important in other cities.In order to test both the robustness and importance of spatial patterns in the MODIS NDVI measurements, a second decision tree was fitted utilizing a discrete, regional variable which defined each city as falling into one of five regions across the US . These regional variables serve as a proxy for a number of variables that may be spatially manifest—for example, differing climate across the country. Using the same number of splits (8), this tree had a similar overall fit to the a-spatial model . This type of decision tree approach to policy analysis has the potential to highlight the complexity and interconnected nature of urban policy priorities and their outcomes. This may be an example of what has been termed a “causal cluster”, meaning that there are multiple corresponding forces at play in producing a particular outcome ,74. Furti.e., Open Space policies, Urban Garden policies) were not identified in either model is. One possible explanation for this disconnect is that the scope of such initiatives could be too small to be identified using the coarse spatial resolution (250 m) of our satellite-sensed vegetation data. Implementing methods for systematic, finer-scale vegetative mapping could help to overcome this challenge.Both analyses agree that cities that have identified land availability as a barrier to economic growth also tend to have higher levels of vegetative greenness. A possible explanation for this may be that cities that have indicated that land is a barrier to economic growth may also have strictly enforced regulations on open space that limit land availability but increase greenness. Finally, land conservation policies were identified by both analyses as being able to distinguish green from grey cities. While this is not surprising in and of itself, the fact that other policies  in distinguishing green from grey cities in the Mid-Plains region than other regions. Conversely, Quality of Life initiatives and trail initiatives were more important in cities located in regions other than the Mid-Plains. While these regional differences may be indicative of how policy decisions can vary in their impact across different geographies, further work is necessary to draw causal connections. Further, explicit incorporation of climate variables across cities could better elucidate the drivers behind regional differences.Decision tree interpretation can be challenging, and the path-dependent nature of the decision tree must be acknowledged. For example, while having conservation-based land use policies results in higher than average vegetation cover, this is only true for cities that also do not have a tourism-based economy, do not have strong economic policies, do not have trail initiatives, and do not have social justice policy initiatives. This limitation is a key reason behind the importance of splits located higher in the decision tree.While both the ANOVA and decision tree analyses agree that having general economic policies in place is related to lower vegetative greenness, the decision tree further elucidates specific types of economic policies that may be important. For example, the decision tree suggests that having a tourism-based economy is associated with higher levels of greenness, as is an institutionally-based economy. The directionality here is not necessarily straightforward, as cities with higher levels of greenness may attract tourism and universities, while at the same time these sectors may also be more supportive of prioritizing green amenities. Further research is needed to determine how these relationships unfold in particular cities.Not all urban environments are the same. Ranging from densely populated urban-industrial complexes to sparsely populated towns, the way different cities relate to their environment is reflected in their place along the continuum of urbanization. Understanding the economic and policy characteristics underlying the relationship between urbanization and vegetative land cover is one important contribution towards complicating traditionally held views of the rural-urban divide. Our results show the range of economic characteristics and policy initiatives that are associated with the urban green-to-grey continuum. These are complex relationships, as no single factor is able to completely distinguish between cities with high levels of vegetative greenness and those with low levels. Rather, a range of possible decisions and priorities face each municipality, and the confluence of these decisions can either facilitate or impede the development of a green city. While previous research has focused on understanding the differences in vegetation within cities, this paper has provided important insights into the differences in vegetation between cities along the urbanization continuum.The aim of this paper is to complicate traditional ideas about the urban-rural divide by examining the policy priorities and economic characteristics of cities at different points along the urbanization continuum. As cities continue to grow and evolve, vegetative greenness provides a novel entry point for examining the heterogeneous nature of urbanization and the policies and priorities that guide these processes. Greenness has significant links to a variety of environmental and human outcomes and is a key feature of the new urbanization continuum. This paper identified what economic and policy differences exist between “green” and “grey” cities by employing MODIS 250 m-resolution NDVI data in conjunction with ICMA sustainability and economic survey results. We found that: (1) cities that have a high focus on economic initiatives tend to be less green; (2) cities that have a focus on social justice initiatives tend to be more green, but are very rare (11% of sample cities); and (3) the specific economic base of a city can aid in distinguishing between green and grey cities, but only under some conditions. These findings suggest that the economic characteristics and governance priorities of cities correspond on a national scale with variation in vegetative greenness, and combinations of factors—or causal clusters—underlie these relationships. Future research using finer resolution information could both validate these findings and elucidate how vegetative structure has changed over time. The new urbanization continuum is a product of economic and political decision making, and understanding these relationships is critical to realizing greener, more sustainable cities of the future.


Pair-wise STF between three groups varied from 0.223 (P1 vs P2) to 0.453 (P2 vs P3). With reference to the global classification of rice cultivars, two major groups (Indica and Japonica) were identified in NE Indian germplasm. The aromatic accessions from Assam, Manipur and Sikkim were assigned to the Indica group, while the accessions from Nagaland exhibited close association with Japonica. The tai accessions of Mizoram along with few chakhao accessions collected from the hill districts of Manipur were identified as admixed. The results highlight the importance of regional genetic studies for understanding diversification of aromatic rice in India. The data also suggest that there is scope for exploiting the genetic diversity of aromatic and quality rice germplasm of NE India for rice improvement.The North-eastern (NE) India, comprising of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura, possess diverse array of locally adapted non-Basmati aromatic germplasm. The germplasm collections from this region could serve as valuable resources in breeding for abiotic stress tolerance, grain yield and cooking/eating quality. To utilize such collections, however, breeders need information about the extent and distribution of genetic diversity present within collections. In this study, we report the result of population genetic analysis of 107 aromatic and quality rice accessions collected from different parts of NE India, as well as classified these accessions in the context of a set of structured global rice cultivars. A total of 322 alleles were amplified by 40 simple sequence repeat (SSR) markers with an average of 8.03 alleles per locus. Average gene diversity was 0.67. Population structure analysis revealed that NE Indian aromatic rice can be subdivided into three genetically distinct population clusters: P1,  Oryza sativa L.) is rich in genetic diversity at both inter- and intra-specific levels. The genetic structure of rice is well characterized , indica  and tropical japonica [Tarai region of Nepal [Rice . In a markers ,4,5,6,7.japonica ,9. The cof Nepal . From thof Nepal ,12,13. Ajoha, chakhao and tai cultivars grown in the states of Assam, Manipur and Mizoram, respectively. The traditional aromatic rice cultivars of Assam are locally known as joha. It is a special class under sali or winter rice. The joha rice cultivars are popular for its unique aroma, good cooking qualities and excellent palatability [chakhao (chak = rice and ahoba or hao = delicious). These landraces are grown both under upland and lowland ecologies. The population genetic analysis of chakhao rice accessions of Manipur have been conducted recently [tai rice cultivars of Mizoram are aromatic in nature and are mostly cultivated under wetland conditions. The aromatic rice germplasm collected from the state of Nagaland has been phenotypically characterized. This rice germplasm has considerable variation in grain size, shape, awn and glume characteristics [Krishna Bhog, Brimphul, Kalanunia and local Basmati are grown in the states of Sikkim and Tripura. The aromatic and quality rice cultivars are mainly used for preparing special dishes in festive occasions and marriage ceremonies.The NE India is a large geographical area comprising of eight states namely Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura. This region is surrounded by Bhutan and China in the North, Myanmar in the East and Bangladesh in the Southern side. The NE India is characterized by high rainfall, humidity, varied topography, heavy natural selection pressures and environmental stresses. Being a biodiversity hotspot , the regtability . In the recently . The taieristics . A consiKnowledge regarding the extent of genetic variation and genetic relationships between genotypes are vital for designing effecting breeding and conservation strategies. Population genetic analysis of rice landraces collected from a region also helps in understanding the complex interaction between rice diversity and human cultivation practices and culture, as the cultivar structure is shaped by the interplay between adaptation to the local environment and artificial selection imposed by the farmers. In contrast, the global studies though provided an excellent overview of the genetic structure of cultivated rice, very often include only a few cultivars from a region and thus unable to explain the rice diversity at a local scale. A large collection of rice germplasm including the aromatic and quality rice landraces from NE India is conserved at National Genebank, National Bureau of Plant Genetic Resources (NBPGR), New Delhi. These valuable aromatic rice germplasm has not been characterized at molecular level.In the present study, a diverse set of 107 aromatic and quality rice accessions collected from various parts of the NE India were analysed using 40 genome-wide SSR markers. The objectives were (i) to estimate the level of genetic diversity and population structure and (ii) to address the genetic classification of the NE Indian rice accession in the context of structured global rice cultivars.indica, aus, aromatic, temperate japonica, tropical japonica and admixed cultivars originating from India and its neighbouring countries) were selected from the study conducted by Garris et al. [A total of 107 aromatic/ quality rice accessions  were sampled from the National Genebank, NBPGR based on characterization data and passport information. The information on the names, accession number, collection sites, kernel length, kernel shape, aroma level and inferred group as determined with STRUCTURE  of each s et al.  . Detailed information about these markers is given in Total genomic DNA was extracted from five day-old seedlings germinated from five well developed seeds of each accession using Qiagen DNeasy plant mini kit  following the protocol provided by manufacturer. Forty SSR markers distributed across the rice genome were designed from the ‘Gramene’ marker database  and PCR conditions were: 5 min at 94°C; 35 cycles of 30 s at 94°C 30 s at annealing temperature and 30 s at 72°C; followed by 7 min at 72°C. The amplified products were visualized by ethydium bromide stained 2.5% agarose gels in BioRad GelDoc XR system.Polymerase chain reaction (PCR) was performed in a total volume of 25 μl containing 50 ng of template DNA, 0.2 mM of dNTPs, 2.5 mM MgCl2, 2.5 μl 10X PCR buffer, 2.5 pmol of each primer, 1.0 unit of http://www.gramene.org/marker/microsat/). In case of non-amplification, we repeated the PCR to exclude technical failure and in case of failure in both PCRs a null allele was recorded. Genotype data of all accessions are given in The molecular sizes (in nucleotide) of the amplified alleles were determined based on migration relative to the DNA ladder using the PyElph-1.4 gel image analysis software . The banThe average number of alleles per locus, major allele frequency, gene diversity, heterozygosity and polymorphism information content (PIC) were calculated using PowerMarker V3.25 .K) evaluated here ranged from 1 to 10. The analysis was performed using five replicate runs per K value, a burn-in period length of 5000, a run length of 50000, and a model allowing for admixture and correlated allele frequency. ‘Structure harvester’ programme (http://taylor0.biology.ucla.edu) was used to determine the final K value  based on both the LnP(D) and Evanno’s ΔK [K = 3 were then performed with a burn-in period of 10000 and a run length of 100000. At K = 3, the membership coefficient from the run with the lowest likelihood value (-9442.1) was used to assign each accession to the K = 1 to 3 populations based on the highest membership coefficient. The STRUCTURE analysis of the combined dataset  was performed following same procedures and run conditions. The graphical display of the structure results was generated using DISTRUCT programme [Population structure was examined using the Bayesian model-based approach implemented in STRUCTURE V2.3.4 ,22. The nno’s ΔK . Subsequrogramme .FST) were estimated using Arlequin V3.5 [Pair-wise values of F-statistics  ranged from 0.27 to 0.88, with an average of 0.67. The observed heterozygosity (Ho) was 0.01. The polymorphism information content (PIC) for the SSR loci ranged from 0.26 (RM178) to 0.87 (RM259).In this study, 40 SSR markers distributed over all 12 chromosomes of rice were genotyped in 107 accessions of aromatic and quality rice landraces of NE India. The summary statistics of the 40 SSR markers are given in K method supported the presence of three genetically distinct clusters , nine were from Mizoram (tai accessions), five were from Sikkim, and one (cv. Amo) from Arunachal Pradesh. This group was dominated by accessions with short to medium kernel length, bold to medium kernel shape and high aroma. P2 had 22 accessions, most from Nagaland except one (cv. Khaw) from Arunachal Pradesh. They have long to extra-long kernel length, predominantly bold kernel shape and medium aroma. P3 contained 16 accessions, all from Manipur (chakhao accessions). This group was dominated by medium to long kernel length, medium kernel shape and medium to high aroma. The three sub-populations (P1–P3) had an Fst  of 0.208, 0.67 and 0.183, respectively with an average value of 0.354, indicating moderate population structure. Overall, 12 (11.2%) admixed accessions were found at K = 3.Bayesian analysis of population structure using the model-based approach in STRUCTURE provided support for the existence of population structure in aromatic and quality rice accessions of NE India. The LnP(D) and Evanno’s ΔThe neighbour-joining (NJ) tree and principal coordinate analysis (PCoA) further confirmed the STRUCTURE results. The tree model-based groups (P1–P3) clearly separated in NJ tree . Note thFST  among populations indicated that 30.5% of the variation was due to differences among groups with the remaining 68.2% due to difference within groups . Around FST  indicateHe = 0.601 and 0.61, respectively; PIC = 0.56 in each population). The genetic diversity in P2 was comparatively low .The amount and organization of genetic diversity differed among the model-based populations . The higK = 1–6) and the accessions were divided into same groupings described by Garris et al. [To establish genetic grouping of the aromatic and quality rice landraces from NE India, a combined dataset including genotyping data from Garris et al.  was subjs et al.   providing the best fit for the data, the population sub-groups were examined for biological relevance from K = 2–5  were apparent within the population. Out of 174 accessions, 100 (57%) were Indica, 42 (24%) were Japonica and 32 (18%) were admixed. Whereas, within NE Indian accessions 68 (63%) were Indica, 22 (21%) were Japonica and 17 (15%) were admixed  also identified as admixed. Note that these chakhao accessions were collected from the hill districts of Manipur. The clustering pattern obtained for K = 3 revealed an association between: (i) group P3 (chakhao) and indica accessions; and (ii) group P2 and japonica accessions. Admixed individuals seem to be prevalent in group P1  value increased with K from 1 to 10 , with hio et al.  also detat K > 4 . Since t K = 2–5 . At K =  admixed . All thegroup P1 . The gro 4 and 5 .aus and indica group  rice landraces collected from Manipur [Genetic diversity and population structure of NE Indian rice germplasm has been analyzed in past using molecular markers ,30,31,32 Manipur . TherefoAnalysis of genetic diversity parameters indicated a high level of variability in the NE Indian aromatic and quality rice collection. The mean number of alleles per locus 8.03) was similar to the estimates reported for 52 Indian aromatic rice cultivars (7.8)  and 83 a was simiindicas [joha rice cultivars of Assam as aromatic indicas and proposed that these cultivars might have evolved via hybridization between indica and Basmati rice varieties. In the present study, population structure analysis revealed three subpopulations (P1–P3) out of which the majority of accession included in P1. This grouping is well in agreement with the genetic distance based clustering and PCoA. Analysis of rice accessions, both aromatic and non-aromatic, from NE Indian states revealed 2–4 clusters. Based on morphological analysis, Vairavan et al. [joha), Mizoram (tai) and Sikkim are mostly short to medium in kernel length, bold in kernel shape and strongly aromatic. These landraces are both genetically and morphologically distinct from those of Manipur (chakhao) and Nagaland. Moreover, the aromatic landraces of Manipur are distinct from that of Nagaland. Combined analysis of NE Indian and global rice accessions using multiple approaches including model-based grouping, NJ tree and PCoA confirmed the assignment of the aromatic accessions from Assam, Manipur and Sikkim to the Indica group, while the accessions from Nagaland exhibited a close association with Japonica. The japonica cultivars have overall lower genetic diversity than indica [tai accessions of Mizoram along with few chakhao accessions collected from the hill districts of Manipur have been identified as admixed. Although STRUCTURE analysis clearly identified the uppermost level of structure for the populations studied here, the difficulties encountered in determining the optimal value of K may obscure the detection of further sub-structuring with real biological significance. From the overall results, it is proposed that the joha, tai and aromatic accessions of Sikkim are intermediate accessions between aus and aromatic. The chakhao accessions are aromatic indicas. The aromatic landraces from Nagaland can be classified as temperate japonicas based on their position in the NJ tree and PCoA and their lower pair-wise FST values with temperate japonicas in comparison to tropical japonicas.The Indian aromatic and quality rice germplasm is comprised of small-, medium-, and long-grain types with mild to strong aroma . Based oindicas . Subsequindicas ,8,11,12.indicas  reportedn et al.  reportedn et al. . Choudhun et al.  reportedn et al.  found fon indica ,41. In tchakhao cultivars of the state of Manipur has export potentiality in South-east Asian countries where consumers prefer sticky rice. In contrast to Basmati rices, the traditional aromatic landraces can be used for a variety of cuisine starting from table rice to deserts, from offerings in various religious ceremonies and social gatherings to diet for the convalescent [In conclusion, SSR marker based molecular characterization of NE Indian aromatic and quality rice landraces revealed that large variation exists among the accessions. The population structure analysis identified three major groups within these landraces which largely correlate with the state-wise grouping as well as farmers’ classification of the landraces. Future population genetics-based studies including extensive collections of rice genetic resources from all the states of NE India will help in exploiting this rice gene pool more effectively in rice improvement programmes. In both domestic and international markets Basmati rices command premium prices, typically two to three times higher than traditional aromatic and quality rice cultivars due to consumer quality preference. Some of the traditional aromatic rice of NE India such as alescent . These lS1 Figa) Mean LnP(D) over five runs for each K value. (b) Rate of change in the log probability of data between successive K values (ΔK).((TIF)Click here for additional data file.S2 Figa) Mean LnP(D) over five runs for each K value. (b) Rate of change in the log probability of data between successive K values (ΔK).((TIF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 Table(DOC)Click here for additional data file.


Cephalotaxus oliveri is an endangered tertiary relict conifer endemic to China. The species survives in a wide range from west to east with heterogeneous climatic conditions. Precipitation and temperature are main restrictive factors for distribution of C. oliveri. In order to comprehend the mechanism of adaptive evolution to climate variation, we employed ISSR markers to detect adaptive evolution loci, to identify the association between variation in temperature and precipitation and adaptive loci, and to investigate the genetic structure for 22 C. oliveri natural populations. In total, 14 outlier loci were identified, of which five were associated with temperature and precipitation. Among outlier loci, linkage disequilibrium (LD) was high (42.86%), which also provided strong evidence for selection. In addition, C. oliveri possessed high genetic variation (93.31%) and population differentiation, which may provide raw material to evolution and accelerate local adaptation, respectively. Ecological niche modeling showed that global warming will cause a shift for populations of C. oliveri from south to north with a shrinkage of southern areas. Our results contribute to understand the potential response of conifers to climatic changes, and provide new insights for conifer resource management and conservation strategies. Global climates including temperatures and precipitation are rapidly changing. The changes of temperature mainly embody in variations in the daily, regional, seasonal, and annual mean temperatures as well as the increase of the intensity, frequency, and duration of abnormally low and high temperaturesPinus massoniana and Pinus hwangshanensisPicea mariana)Temperature and precipitation have been revealed as important drivers for local adaptation for conifers711Cephalotaxus oliveri is an endangered tertiary relict conifer endemic to China with important economical and medical values16Cephalotaxus sect. Pectinate in CephalotaxaceaeCephalotaxus oliveri is a wind-pollinated dioecious woody shrubs or small trees up to 4 m tall with yellow to grayish brown and scaly bark. Its distinguishing features embody in leaves densely arranged on leafy shoots and stomatal bands on abaxial surfaceC. oliveriCephalotaxus in Yunnan20CephalotaxusCephalotaxus oliveri was hypothesized to originate in the Oligocene and its population diversification was associated with the rapid uplift of the Qinghai-Tibetan PlateauC. oliveri is suitable for investigating the adaptive evolution mechanism to climate variation. More importantly, in the light of global climate change, it is conducive to understanding the adaptive potential of C. oliveri and formulating protection strategy.FST to decide whether a particular locus is adaptive262629Due to the lack of genomic resources for nonmodel species, molecular marker-based genomic scans have been widely used to explore adaptive loci. All loci across the genome are generally expected to share the same demographic historyC. oliveri. Because populations sampled represented over a local scale, our results may precisely detect the genetic signatures for adaptation. The goals of the study were (1) to examine adaptive loci in the genome of C. oliveri; (2) to analyze the correlation between climatic variables and adaptive loci; (3) to evaluate the population genetic structure of C. oliveri, and (4) to explore the genetic basis of the adaptation of C. oliveri to climate.In this study, we employed ISSR markers to detect adaptive loci under selection and utilize SAM to identify the association between climatic variations and genetic data. In addition, we also investigated the genetic structure among natural populations of C. oliveri populations. Overall, 310 reliable loci were identified with 100% polymorphic ranging in size from 200 to 2000 bp. A high level of genetic variation was observed with 93.31% polymorphic loci at the species level  was exhibited in HNym population and the lowest  in JXxs population. At the regional scale, Hunan  maintained the highest level of variation and Guangdong  the lowest. The results indicated that C. oliveri possesses a high level of genetic variation.Twenty-one ISSR primers were selected to investigate the genetic structure in es level . The higFST (0.39565) and GST (0.3862). AMOVA results further showed that most genetic variation was occurred within populations , whereas the proportion of genetic variation among populations within regions was 29.87%  . In addition, significant patterns of isolation by distance were revealed by comparing FST values with geographical distances .Populations were significantly structured as revealed by overall < 0.001) . Only 9.C. oliveri were clustered into two groups  = −39912.5, Var[LnP(D)] = 4877)  . Populat = 4877) . Only a 10PO above 2, which is a threshold for decisive evidence for accepting a model under selection, corresponding to a posterior probability greater than 0.99Bayescan identified 32 loci as outliers with a logLinkage disequilibrium (LD) was detected for 28 of the 14225 combinations of all 310 loci with the false discovery rate of 0.1. Twenty of the 310 loci (6.45%) were involved in the detected combinations in LD. In contrast, six (42.86%) of the 14 outlier loci were involved in LD; they were Locus 40, 63, 102, 161, 165, and 206. When two or more linked loci were in LD within a chromosomal region, this region was defined as an LD blockThe logistic regressions of 310 ISSR markers and 19 climatic variables were calculated using the SAM program. With 99.9999% confidence level, on a total of 6510 models computed, SAM identified 20 significant associations (0.31%). Of those, ten loci were significantly related with temperature, and 11 were significantly related to precipitation. Furthermore, five of the 20 loci related with climatic factors were also outlier loci detected by Dfdist and Bayescan . Locus 7C. oliveri were estimated by MAXENT (Maximum Entropy Model). The average AUC test for replicate runs and the standard deviation were 0.955 and 0.019, respectively, which indicated good predictive model performance. Minimum training presence logistic threshold was 0.1270. The predicted current geographical distribution of C. oliveri was generally similar to its actual distribution including Jiangxi, Hunan, Guizhou, Guangxi, and Hubei, even though some predicted areas do not have any records at present . ISSRs are highly variable and have been widely applied to the assessment of population genetic diversity and structure in plants3333363338This study has analyzed the adaptive evolution of C. oliveri and ascertain the relationships between candidate loci and climatic factors. In order to ensure unbiased analysis, only distinct, reproducible ISSR loci were scored in this study. Three hundred and ten loci produced by 21 primers have good genome coverage in C. oliveri. Fourteen outlier loci were identified by both Dfdist and Bayescan. The two complementary and exhaustive methods guaranteed strong confidence of the 14 loci with very stringent significance criteria. The proportion of outlier loci detected in C. oliveri was 4.22%, which conformed to the percentage between 2% to 15% in AFLP genome scan or other molecular markersMikania micrantha from AFLPP. abies) from RAPD markersWe first applied ISSRs to identify adaptive evolution in P. monticola and Keteleeria davidiana var. formosana943P. sitchensis)Abies alba, Larix decidua, P. cembra, and P. mugo, whereas SNPs in L. decidua and P. cembra are found to be related to seasonal maximum temperature and winter and autumn precipitationsP. halepensis)C. oliveriC. oliveri, suggesting that a relatively small number of loci govern climatic adaptation in this species. The result was very similar to previous studies13P. taeda)P. mariana)Temperature and precipitation have been identified to be the major selective pressure driving plant adaptation894344P. mariana), whose four of 26 outlier SNPs were common to both the temperature and the precipitation52P. abies, drought tolerance was found to be genetically correlated with tolerance to freezing temperatureP. mariana, a gene coding for the DnaJ heat shock protein was detected to carry an adaptive SNP related to both temperature and precipitation, and the protein was produced under stresses involving temperature and moistureP. albicaulis) and loblolly pine (P. taeda), annual, seasonal mean temperatures, and rainfall patterns also appear to drive local adaptation, which primarily involved response to both temperature and drought50Cephalotaxus oliveri extends from the west to the east in China, with an environmental gradient showing significant precipitation and temperature differencesC. oliveri has successfully responded and adapted to historic climate changes. The species was inferred to originate in the Oligocene [ca. 28.32 million years ago (Ma)] and diversified in the early Miocene (ca. 17.73 Ma)C. oliveri may have provided raw material for its adaptation to changing climatic conditions. Meanwhile, its high population differentiation possibly also accelerated local adaptation. Our results indicated that C. oliveri populations well adapt to temperature and precipitation factors including annual mean temperature (Bio 1), mean diurnal range (Bio 2), isothermality (Bio 3), max temperature of warmest month (Bio 5), mean temperature of warmest quarter (Bio 10), annual precipitation (Bio 12), precipitation of driest month (Bio 14), precipitation seasonality (Bio 15), precipitation of driest quarter (Bio 17), and precipitation of coldest quarter (Bio 19)  .C. oliveri have a wide geographic range, from south to north is from 25°3′N, 130°45′E to 34°40′N, 111°02′E and east to west from 28°37′N, 114°54′E to 27°37′N, 113°52′E  were found to be involved in LD. Our result lends further support to the theory that LD in forest trees decays rapidly51Linkage disequilibrium (LD) in the genomic region can reflect the genetic signature associated with local adaptation, especially for long-lived plants51D’ and r2 acted quite differently; namely, D’ had a value of 1, but r2 were much smaller http://www.diva-gis.org/) in terms of informatic characterization of C. oliveri. The bioclimatic variables included annual mean temperature (Bio 1), mean diurnal range (Bio 2), Isothermality (Bio 3), temperature seasonality (Bio 4), max temperature of warmest month (Bio 5), min temperature of coldest month (Bio 6), temperature annual range (Bio 7), mean temperature of wettest quarter (Bio 8), mean temperature of driest quarter (Bio 9), mean temperature of warmest month (Bio 10), mean temperature of coldest month (Bio 11), annual precipitation (Bio 12), precipitation of wettest month (Bio 13), precipitation of driest month (Bio 14), precipitation seasonality (Bio 15), precipitation of wettest quarter (Bio 16), precipitation of driest quarter (Bio 17), precipitation of warmest quarter (Bio 18), and precipitation of coldest quarter (Bio 19) (Climatic layers at 2.5′ resolution for current conditions were obtained from the WorldClim database ((Bio 19) .We extracted genomic DNA from tissues using a modified cetyltrimethyl ammonium bromide (CTAB) method with −20 °C propanone pretreatment to eliminate polysaccharides, which was successful for conifers342, 0.25 mM dNTPs, 0.24 μM primer, 1.5 units of Taq polymerase, and DNA-free water. In an ABI veriti thermocycler, PCR started with an initial denaturation at 94 °C for 5 min followed by 40 cycles with 94 °C for 30 s, 53 °C–54 °C for 45 s and 72 °C for 90 s, and ended with a final extension of 7 min at 72 °C. DNA quality and quantity were estimated using 1.7% agarose gel in TAE 1X buffer, stained with Ethidium Bromide. Additionally, 100 bp ladder and negative and positive controls were loaded and run at constant voltage (135 V) for 95 min. After running, the gels were UV visualized and recorded using a Gel Doc 2000 Camera.To cover the widest genomic region and ensure high-quality reproducible bands, an initial screening was developed using one individuals randomly obtained from HNhng population. Twenty-one primers were screened from 100 primers (UBC primer set #9) of the Biotechnology Laboratory, University of British Columbia. PCR amplification was carried out in a total volume of 20 μl consisting of 20 ng of template DNA, 10 mM Tris-HCl (pH 8.3) reaction buffer, 50 mM KCl, 2.0 mM MgClUnambiguous ISSR fragments were transformed into 01 character matrix .ST)POPGENE ver 1.31 was used to calculate genetic parameters. The estimates included the percentage of polymorphic loci (P), Nei’s (1973) gene diversity (H), Shannon’s information index (I), total gene diversity (Ht), and gene differentiation  with 1000 permutations using ARLEQUIN version 3.071K = 1–27 were conducted to estimate the number of clusters (K) with maximum likelihood with the following settings: admixture model, correlated allele frequencies, burn-in length of 100 000, MCMC repetitions of 1000K was defined both using log probabilities [Pr (X|K)] and hoc statistic ΔKTo infer population structure, we assigned individuals and populations to clusters by using the model-based program STRUCTURE 2.3FST values under an island model, which was insensitive to population structure, demographic structure and mutation level. Simulations were computed with a mean FST similar to the trimmed mean FST, which was calculated by excluding 30% of the most extreme FST values observed in the empirical dataset. We compared the distributions of the FST values over all loci to null hypothesis of neutral evolution. Loci with a high or low FST value were considered as potentially under selection. In this study, we simulated the neutral distribution of FST with 50 000 iterations at the 99.5% confidence level. Bayescan developed by Foll and GaggiottiFST based model, which uses reversible jump MCMC and estimation of the Bayesian posterior probabilityFST values. Large FST is then interpreted as signature of local adaptation. We calculated outliers using a burn-in of 50 000 iterations, a thinning interval of 20, and a sample size of 5 000.To identify loci under selection, software Dfdist and Bayescan were used to ensure truly adaptive regions of the genome. The software Dfdist was applied to simulate a null distribution of r2) implemented in TASSEL 2.2 Linkage disequilibrium (LD) between all pairs of ISSR loci was calculated by the squared allele frequency correlation coefficient (D’), as well as the squared correlation coefficient (r2) and p values based on Fisher’s exact test. D’ determined whether recombination had occurred between a pair of allelesr2 was the conventional 0.1r2 were provided by the p value calculated in TASSEL.The evaluated estimates included the standardized disequilibrium coefficient (http://www.econogene.eu/software/sam). The likelihood ratio (G) and Wald tests were used to determine the significance of the models. The null hypothesis was designed so that the above two statistical parameters conformed to a normal distribution. A model was considered significant only if the null hypothesis was rejected by both statistical tests at the 95 and 99.999% confidence level. The 310 ISSR markers in all 22 C. oliveri populations were examined against 19 climatic variables in the SAM analysis.To investigate the association between ISSR genetic data and climatic variables, SAM was employed . After removing duplicate records, it remained a total of 57 records of C. oliveri that were used to generate the distribution model by using 19 bioclimatic data layers from the WorldClim database (http://www.worldclim.org) at 2.5-arcmin resolution  as a general circulation model under two climate scenarios (IPCC-CMIP5 RCP 4.5/8.5) to ensure the accuracy of evaluation. The RCP 8.5 scenario represents a higher predicted greenhouse gas emission than RCP 4.5MAXENT was run according to the following settings: random test percentage = 25; regularization multiplier = 1; convergence threshold = 0.00001; maximum iterations = 1000 and averaged across 10 cross-validation runs. Ultimately, variable importance was determined by a jackknife test. We evaluated the accuracy of each model prediction by calculating the area under the curve (AUC) values. AUC is an efficient indicator of model performanceHow to cite this article: Wang, T. et al. Local adaptation to temperature and precipitation in naturally fragmented populations of Cephalotaxus oliveri, an endangered conifer endemic to China. Sci. Rep.6, 25031; doi: 10.1038/srep25031 (2016).