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Identify this tree from Nepal

Identify this tree from Nepal


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This is a picture of a tree in Nepal. Can somebody identify it?


Gray tree frog

The gray tree frog's color changes in response to its environment and activities, and can range from green to gray or brown. The upper surface of the body has a blotchy pattern that resembles lichen. Although the pattern varies, it usually features two dark central patches, which can be green, buff or gray. These frogs have a white spot beneath each eye and a dark stripe from the rear of the eyes to the front of the legs. The snout is short, and the skin is warty and coarse.

The upper surfaces of the legs feature a dark, banded pattern, which contrasts starkly with the bright yellow or orange undersides of the legs. Scientists believe the bright coloration serves as a warning for predators not to attack. The gray tree frog has webbed hands and feet. The enlarged tip of each digit produces an adhesive fluid that allows this species to better grip trees and improves its climbing abilities. The frog's belly is white, although the male reveals a black throat when it is calling.

Like the adult, the gray tree frog tadpole has inconsistent coloring, including different shades of brown or olive green. As tadpoles, they are scarlet or orange-vermilion with black blotches around the edge of the crests. The body and tail are patterned with many specks of black and gold. As the individual ages, it develops its adult coloration.

Adult male gray tree frogs are around 1.25-2 inches (32-52 millimeters) in length. Females are typically slightly larger than males, ranging from 1.5-2.25 inches (38-60 millimeters) in length.

The gray tree frog's range covers much of the eastern United States, from northern Florida to central Texas and north to parts of southeastern Canada. It is a largely arboreal species that occupies a variety of wooded habitats and is frequently found in forests, swamps, on agricultural lands and in backyards.

Access to trees and a water source is common to all habitats it occupies. When a gray tree frog is young and newly metamorphosed, it usually remains near the forest floor. As it ages, it may transition to living in the forest canopy.

Males emit a loud, musical call, usually after dusk, for as long as four hours. The male uses the call to establish a breeding territory and to find a mate.

Adult gray tree frogs mainly prey upon different types of insects and their own larvae. Mites, spiders, plant lice, snails and slugs are common prey. They may also occasionally eat smaller frogs, including other tree frogs.

They are nocturnal and hunt in the understory of wooded areas in trees and shrubs. As tadpoles, they eat algae and organic detritus found in the water.

A male begins the mating call in early spring, shortly after emerging from hibernation. In the mid-range areas males begin calling in late April to early May. Males call to females from trees and bushes that are usually close to, or overhanging, streams or standing water.

The exact timing of breeding for gray tree frogs varies based on temperature and their location throughout the range. Most reproduction takes place early on, although the calling season lasts from late April to early August. Individuals may mate up to three times in a season.

Males are very territorial and will fight other males to defend their area. Fights may last 30 to 90 seconds and consist of wrestling, shoving, kicking and head butting until the subordinate male retreats. Females instigate mating by approaching a calling male and touching him before rotating 90 degrees.

The individuals engage in amplexus, a mating position in which the male grasps the female with his front legs, as the female deposits 1,000 to 2,000 eggs which are externally fertilized by the male. Since mating occurs while the frogs are floating in water, eggs are deposited into the water in small clusters, which attach themselves to structures via a transparent, mucous outer layer.

Tadpoles usually hatch after three to seven days, depending on the water temperature. About 10 minutes to an hour before hatching, the embryo has to release a fluid to help break down the wall of the egg. Tadpole development depends on water temperature with metamorphosis typically occurring in 45 to 65 days. They become sexually mature after two years.


What are some examples of types of acacia tree?

Acacia senegal – Gum Arabic, Senegal Gum, Sudan Gum Arabic

This plant produces gum arabic. This resinous substance has many applications in pharmaceuticals, food science, and traditional medicine.

This species of acacia is Australia’s national flower! The yellow globes of its bloom are actually many small flowers.

This low growing acacia grows white flowers in beautiful orbs.


The evolution of traditional knowledge: environment shapes medicinal plant use in Nepal

Traditional knowledge is influenced by ancestry, inter-cultural diffusion and interaction with the natural environment. It is problematic to assess the contributions of these influences independently because closely related ethnic groups may also be geographically close, exposed to similar environments and able to exchange knowledge readily. Medicinal plant use is one of the most important components of traditional knowledge, since plants provide healthcare for up to 80% of the world's population. Here, we assess the significance of ancestry, geographical proximity of cultures and the environment in determining medicinal plant use for 12 ethnic groups in Nepal. Incorporating phylogenetic information to account for plant evolutionary relatedness, we calculate pairwise distances that describe differences in the ethnic groups' medicinal floras and floristic environments. We also determine linguistic relatedness and geographical separation for all pairs of ethnic groups. We show that medicinal uses are most similar when cultures are found in similar floristic environments. The correlation between medicinal flora and floristic environment was positive and strongly significant, in contrast to the effects of shared ancestry and geographical proximity. These findings demonstrate the importance of adaptation to local environments, even at small spatial scale, in shaping traditional knowledge during human cultural evolution.

1. Introduction

The ability to learn from others and to transmit knowledge and skills has shaped human history [1–3]. Transmission of traditional knowledge underpins both long-term conservation and rapid change in cultural traits, enabling humans to refine survival strategies and occupy diverse habitats [4,5]. Two modes of transmission of traditional knowledge have been described. Traditional knowledge is passed from generation to generation, and so from ancestral to descendant cultures, in what is termed ‘vertical transmission’. Selective diffusion or borrowing is referred to as ‘horizontal transmission’, and serves to modify traditional knowledge, although innovation in the absence of horizontal transmission may also modify an ethnic group's knowledge. The combination of modification and vertical and horizontal transmission establishes bodies of traditional knowledge unique to each culture, but still reflecting the traditional knowledge of their ancestors [4,6].

The study of the evolution of human culture is a matter of considerable current interest, particularly as phylogenetic methods borrowed from biology are finding wide application. Phylogenetic methods have shed light on the transmission of human culture, treating cultural traits as analogous to biological traits [6–8], and modes of inheritance of cultural traits are beginning to be ascertained for aspects of human culture. Those passed on vertically include some aspects of material culture [9,10] and family and kinship organization [4,11] traits transmitted horizontally include cases of technical innovations [12], music [13] and other aspects of material culture [14].

Some behavioural and cultural traits show correlation with environmental factors, revealing the adaptation of traditional knowledge to the environment [15,16]. Two patterns of cultural trait distribution are suggestive of trait evolution in response to environment: traits differing between closely related cultures found in different environments, and traits similar between unrelated cultures sharing an environment may be traits which are adapted to the environment. Ecological correlates of behavioural and cultural traits reveal adaptation of this kind [15,16]. The environment may necessitate cultural innovation, but it also influences cultural traits by imposing functional constraints [17].

Medicine is an important element of traditional knowledge which includes indigenous healthcare traditions, beliefs and various practices. Well over half of the world's population depend on traditional medicine for healthcare, up to 80% in countries of the developing world [18]. Between 10 000 and 53 000 plant species are used in traditional medicine, and use of plants in medicine is a ubiquitous and important cultural trait [19,20]. Because not all plants are found everywhere, the floristic environment constrains plant use. The adaptation of traditional medicine when migrations expose cultures to new floristic environments may occur through horizontal transmission of plant use and homogenization of practices [21–23]. In this study, we focus on 12 moderately to closely related ethnic groups from Nepal. With approximately 75 ethnic groups and approximately 7000 plant species, Nepal has remarkable cultural [24,25] and floristic diversity (http://www.floraofnepal.org) [26,27]. One in seven plants is or has been used in some sort of medicinal preparation [24]. By performing comparisons of traditional plant use among closely related cultures, the aim of this study is to shed light on the evolutionary processes that have shaped this body of traditional knowledge.

Our study investigates how similarities among the medicinal floras of 12 Nepalese ethnic groups reflect affinities of the floristic environments to which these ethnic groups are exposed, their cultural affinities and their geographical proximities (figure 1). Studies of closely related groups are ‘potentially the most informative for testing cross-cultural hypotheses’ [7], since both vertical and horizontal transmission can occur when closely related cultures are found within a region. For the 12 Nepalese ethnic groups, we use linguistic affinities as a proxy for ancestry, and calculate geographical distances based on their distribution in the country to investigate the effect of geographical separation on the composition of medicinal floras. By performing comparisons of traditional plant use among closely related cultures, this study aims to shed light on the evolutionary processes that have shaped this body of traditional knowledge while explicitly evaluating the role of the environment in shaping the evolution of traditional medicine. In the context of burgeoning research aimed at elucidating the modes of inheritance of cultural traits, we put forward and test an approach we develop to disentangle the spatial, environmental and historical determinants of traditional knowledge.

Figure 1. Cross-cultural similarities in traditional medicine can be determined by shared ancestry, geography or the environment. The relationships of six hypothetical cultures, shown in circles of different colours, are represented using a cultural phylogenetic tree (a), and (b) shows their distribution in a hypothetical region, where two types of environment (black and white) are present. Mortars and pestles represent traditional medicinal systems. If cultural ancestry determines similarities in traditional medicine, closely related cultures will end up with similar traditional medicinal systems (c). However, if those similarities are determined by the environment, those medicinal systems will reflect environmental similarities (d). Finally, if horizontal transmission shapes traditional medicinal systems, then cultures in close geographical proximity will share traditional medicinal systems (e).

2. Material and methods

(a) Cultural distances

The 12 ethnic groups from Nepal under study were Chepang, Danuwar, Gurung, Lepcha, Limbu, Magar, Majhi, Raute, Sherpa, Sunuwar, Tamang and Tharu. These groups represent more than a quarter of the total population in the country and the two main language families in Nepal, namely Indo-European (Danuwar, Majhi and Tharu) and Sino-Tibetan (Chepang, Gurung, Lepcha, Limbu, Magar, Raute, Sherpa, Sunuwar and Tamang). Languages from these two groups are spoken by approximately 98% of the Nepalese population. To calculate cultural distances among these ethnic groups, we used linguistic data, demonstrated to be a good proxy for relationships of human groups [28,29]. We selected ethnic groups with language retention (strong one-to-one relationship between ethnic communities and language) [30] and therefore correspondence between language and cultural ancestry is maximized. A matrix of pairwise distances of the languages was created based on language grouping information extracted from the Ethnologue [31], a worldwide dataset and classification of languages. Pairwise cultural distance was calculated using the number of hierarchical levels of language groupings separating the ethnic groups' respective languages, summarized in figure 2. Cultural distances are shown in the electronic supplementary material, table S1.

Figure 2. Classification of the 12 languages of ethnic groups under study and their distribution in the floristic areas of Nepal. Classification based on language data acquired from the Ethnologue [31]. Pairwise cultural distances among ethnic groups were calculated by counting the language subgroupings (shown in circles) that any given language pair does not share. For example, the distance between Tamang and Gurung is zero, as they are placed in same language group (Tamangic) and share all language subgroupings. The distance between Tamang and Lepcha is three, as there are three language subgroupings they do not share (Tamangic, Tibetic, Lepcha).

(b) Medicinal floras and floristic environments

Ethnomedicinal information was collated from nationwide compilations of ethnobotanical plants [24] (http://www.eson.org.np/database/index.php). All plant species used by each of the 12 ethnic groups were recorded and presence or absence of usage was scored at the genus level for each ethnic group. To assign floristic environments to ethnic groups, each ethnic group was located in one or more of the three major biogeographical regions of Nepal (western, central and eastern) depending on its distribution in the country [25], as shown in figure 2. The floristic environment of each group was all the species found across its range over these three biogeographical regions in Nepal. Data on the distribution of plant species in the biogeographical regions were collated from a checklist of the flora of Nepal [26].

(c) Phylogenetic analyses

To estimate phylogenetic distances between all pairs of medicinal floras and all pairs of floristic environments, we used a genus-level phylogeny of the flora of Nepal from a previous study, which included 1335 genera, representing more than 85% of the Nepalese flora [32]. The phylogeny includes one exemplar species per genus. Where possible, that species was from the local flora, but in cases where a DNA sequence or plant material was not available, species of the same genus from other localities were used. The tree is based on sequence data from the plastid DNA marker rbcL, which were analysed under the maximum likelihood (ML) criterion [33]. For more details on taxon sampling, molecular techniques and phylogenetic reconstruction, see [32].

Pairwise distances for floristic environments and medicinal floras were calculated using the ‘comdistnn’ command in P hylocom v. 4.1 [34] on the phylogenetic tree of the flora of Nepal. This command calculates the phylogenetic distance between two samples based on the nearest-neighbour phylogenetic distance. For each taxon in one sample, the algorithm finds the closest relative in the other sample and records the phylogenetic distance between them. The final value for the two samples is the average of these distances for all taxa in both samples. Our samples were either the floristic environment of a region or the medicinal flora of an ethnic group. Large values acquired from ‘comdistnn’ denote that the two samples do not include many taxa that are particularly closely related, whereas small values indicate close relationships between two samples. Genera included in medicinal floras, but not sampled in the phylogeny were excluded from these analyses. The distance matrices for floristic environments and medicinal floras are shown in the electronic supplementary material, tables S2 and S3, respectively.

(d) Geography

We calculated geographical distance between all pairs of cultures using the haversine formula [35] on the midpoint locations of each culture. The haversine formula calculates the great-circle distance between two points on the globe, i.e. the shortest distance between them on the surface of the globe. In our study, it is the great-circle distance between midpoint locations of cultures. The geographical distributions of cultures were obtained from the Ethnologue [31]. When there were multiple cultural variants (e.g. Gurung, Magar, Tamang and Tharu), we calculated the distance from each variant to the relevant culture and averaged these distances. The distance matrix is shown in the electronic supplementary material, table S4.

(e) Statistics and simulations

The four distance matrices (1, culture 2, floristic environment 3, medicinal flora and 4, geography) were used to perform five correlations: 1, floristic environment versus medicinal flora 2, culture versus geography 3, geography versus floristic environment 4, culture versus medicinal flora 5, geography versus floristic environment. Pearson product-moment correlation coefficient (r) and significance of correlations between these matrices were estimated with Mantel tests [36] in the ‘vegan’ package in R [37]. Table 1 summarizes the results of these correlations.

Table 1. Pearson product-moment correlation coefficient (r) and significance (p) of correlations among distance matrices. Floristic environment refers to the distance matrix between the total floras found in each floristic region. Medicinal flora refers to the distance matrix between the medicinal floras used by each ethnic group. Culture refers to the distance matrix describing cultural relatedness, and based on linguistic affinities. Geography refers to the distance matrix describing geographical proximity of different cultures. n.s., non-significant.

To test whether the absence of signal of vertical transmission of medicinal floras in our study can be attributed to methodological artefact, we generated an artificial dataset that represents an extremely conservative mode of vertical transmission. We generated a random ‘medicinal flora’ composed of a set of 66 genera (similar size to the medicinal floras in our dataset). This was used as the ancestral medicinal flora of all cultural groups. Starting from an ‘ancestral’ language node, we ‘evolved’ the medicinal flora in the following way: every time we came across a language bifurcation, we substituted one of the genera in the medicinal flora with its closest relative from the phylogenetic tree of the flora of Nepal. The resulting medicinal floras associated with the extant languages were between 3 and 17% different from one another (the range of distances in observed medicinal floras was 18.5–87%). Pairwise phylogenetic distances were calculated with the ‘comdistnn’ option in P hylocom v. 4.1, as described above, and the correlation of these distances with linguistic distances was assessed with a Mantel test.

3. Results and discussion

Five tests for correlations between four matrices of pairwise distances show unequivocally that the floristic environment to which a culture is exposed exerts the strongest influence on the medicinal flora adopted (table 1). The statistically significant correlation between floristic environment and medicinal flora (r = 0.73, p < 0.001 table 1) provides strong evidence for this link. The distance matrices used in this test, one to describe relatedness of floristic environments and the other relatedness of medicinal floras, were both calculated using a genus-level phylogenetic tree of the flora of Nepal encompassing 85% of the total flora. For the floristic environment, relatedness was calculated using the phylogeny and published Nepalese plant distribution data. Medicinal floras of the 12 ethnic groups were superimposed onto the phylogenetic tree to calculate the relatedness of the medicinal floras. Correlation between these distances was assessed using a Mantel test [36], an approach used in similar studies [6,38].

The correlation between floristic environment and medicinal flora could be attributed to spatial autocorrelation, Galton's problem of closely related cultures being spatially proximate, a problem which has long bedevilled comparative cultural studies [39]. To evaluate whether Galton's problem is confounding our results, our second Mantel correlation test is crucial: using language as a proxy for cultural relatedness [(8, 16), we show there is no significant effect of geographical structure of ethnic groups in Nepal (r = 0.13, p > 0.05 table 1). To perform this test, cultural relatedness among the 12 ethnic groups was calculated based on language affinities (figure 2), and the geographical proximities of pairs of cultures were calculated between the midpoints of their ranges. The lack of geographical clustering of related cultural groups shows that in Nepal we do not conflate cultural relatedness and geographical proximity, overcoming spatial autocorrelation when we seek to assess the determinants of a culture's selection of medicinal plants. The dispersal of related cultures, alongside Nepal's cultural diversity and rich medicinal flora, makes the Nepalese case an exceptionally powerful one for teasing apart the factors influencing the adoption of a medicinal flora.

Further Mantel tests also contribute to the robust interpretation of the correlation between floristic environment and medicinal flora. Background signal of shared ancestry could contribute to our finding of environmental convergence of medicinal floras. However, we found culture and medicinal flora not to be strongly correlated (r = 0.21, p > 0.05 table 1), revealing that vertical transmission over the time scale at which ethnic groups diverge is not the main determinant of traditional medicinal knowledge. Even when taking into account the possibility of using close relatives to substitute for unavailable plants in different environments, presumed ancestral use—as would be revealed by related cultures selecting the most similar sets of plants—is not the main determinant of plant use by a culture.

Environmental convergence of medicinal floras could also be because of horizontal exchange between cultures. Nevertheless, we found that pairwise distances describing the geographical proximities of cultures and the relatedness of medicinal floras are not significantly correlated (r = 0.48, p > 0.05 table 1), indicating that horizontal transmission, or borrowing of traditional knowledge, which would be most probable between cultures with overlapping distributions, is in fact not important here.

Overall, our findings indicate that through their history, the 12 Nepalese ethnic groups under investigation have adopted a flexible approach and incorporated new, unrelated plants into their medicinal floras. Although signal of both vertical and horizontal transmission was found in the medicinal floras of the 12 ethnic groups under investigation, this was very weak and not significant. Instead, we found strong human responses to similar floristic environments. Our finding that related cultures are not geographically structured also indicates that closely related cultures can occupy different floristic environments, therefore driving innovation in medicinal plant use.

The spatial level used to delineate floristic regions was the three-zone longitudinal division of Nepal (western, central, eastern biogeographical regions), as finer botanical distribution data across the whole of Nepal are not yet available. This delimitation of floristic regions could weaken our study. Nevertheless, there was a strong correlation (r = 0.58, p < 0.01 table 1) between geographical proximity of cultures and their floristic environments. This shows that our delimitation of floristic regions is meaningful: our data recover cultures in close geographical proximity being exposed to similar plants, even though cultures have distributions that do not map to our crude floristic regions. Of course, there is considerable latitudinal variation for both the plants and the ethnic groups within those regions, but this issue cannot be tackled until finer distribution data are available.

Our study depends on the recognition of ethnic groups, and the accurate estimation of the relationships between them. Here, we use language affinities to infer relationships between ethnic groups. Language affinities have often been used as a proxy for intra-cultural relationships, but increasingly quantitative linguistic models are used [40–42]. Detailed linguistic studies do not exist for the Nepalese groups in our study, so our estimates of relatedness might be unreliable. Studies comparing traditional language classifications, as used here, and Bayesian phylogenetic estimates, reveal striking congruence between the two classifications [42,43]. Whether this would be true in our case is not known, and comparative linguistic studies are needed to consolidate our understanding of historical intra-cultural relationships in Nepal. In terms of the recognition of ethnic groups, anthropologists are revealing ethnic group identities in Nepal to be very fluid, challenging the idea of fixed ethnic groups [44]. While a particular village may have historical depth in a place, the knowledge carried by an individual informant may well derive from a complex ancestry and social heritage [44,45]. If the ethnic groups we recognize have in fact experienced cultural exchange, then we would expect a signal from horizontal transmission. Our study does not reveal a strong relationship between the geographical proximity of cultures and their medicinal flora, so if this horizontal exchange is occurring it does not overwhelm the impact of the floristic environment. Cultural exchange may be occurring between cultures that are not proximate, due to long-distance migration of individuals to new cultures. Cultural exchange of this kind, where individuals migrate to either geographically distant cultures or cultures that are not linguistically related, would not be revealed by our approach. Further detailed studies of individuals' ethnobotanical knowledge and heritage would be needed. This shortcoming also highlights limitations with the sourcing of ethnobotanical knowledge for this study. Our ethnobotanical data were sourced from publically available resources, which do not take into account the number of informants or the personal heritage of informants. Thus, the ethnobotanical data used may introduce biases into our study. Meta-analyses of the kind presented here highlight the importance of further, detailed ethnobotanical work on the ground.

Finally, a limitation lies in the interpretation of results from Mantel tests, which have been shown to be prone to type-I and type-II errors [46,47]. Moreover, if one variable is poorly measured, its correlation with other variables might be reduced. We have already highlighted that our use of language affinities as a proxy for intra-cultural relationships may be a weakness. If our estimates of ancestry are misleading, this could explain the absence of significant correlation between ancestry and other traits. Despite the pitfalls of Mantel tests, they are still used very commonly in comparative cultural studies, as other similar methods, such as independent contrasts, are oversensitive to horizontal transmission [47].

In spite of the caveats discussed above, we reveal an experimental, flexible approach in the macro-evolution of traditional knowledge, and the strong influence of the natural environment. Similarly strong environmental influences have been observed for other cultural characters [15], reflecting geographical barriers to cultural transmission [38] and adaptation of human populations to different environments [11]. Our study highlights independent adoption of similar medicinal plants when human are exposed to similar floristic environments. Other studies have attributed independent adoption to independent discovery of plant bioactivity [32], but between much more distantly related cultures, and therefore over a much longer time scale than is inferred here. The experimental and flexible approach revealed here is especially intriguing given the importance of traditional medicine for healthcare, and previous research showing that functional cultural characters are conserved [17,48]. However, the role of traditional medicinal knowledge is to contribute to the fitness of human populations in different environments through healthcare [49]. As availability of resources varies among environments, medicinal floras need to be adapted to local environments in order to respond to healthcare needs [21,23,50].

Our study is unique among studies of bio-cultural evolution in incorporating both a cultural and a biological phylogenetic tree to investigate the transmission of a key cultural trait. We use the phylogeny of Nepalese flowering plants to calculate similarities between the floristic environments that ethnic groups are exposed to—a proxy for environmental similarity. We then calculate similarities between medicinal floras using the same phylogeny. Phylogenetic measures are particularly appropriate for our study, since closely related ethnic groups exposed to novel floristic environments often use close relatives of their ancestral medicinal plant species [21,51]. Selecting relatives in this way is a form of vertical transmission. The strength of this study lies in the use of biological phylogenies to estimate the relatedness of medicinal floras. Conventional taxonomic approaches based on the number of species in common will not capture underlying similarities between medicinal floras. Some studies have shown that during human migrations, closely related plants are selected to substitute for plants that are not available in the new floristic environment [50–52]. If, as has been suggested, phylogeny underlies peoples' selection of medicinal plants [32], taxonomic approaches could overemphasize the differences between medicinal floras, while phylogenetic measures would consider substitution by a closely related plant to be attributed to vertical transmission of knowledge (see electronic supplementary material, figure S1). To test whether the use of a plant phylogenetic tree is capable of identifying this type of signal, we created a simulated dataset where differences in medicinal floras are the result of vertical transmission, but which accommodates the selection of closest relatives by sister cultures exposed to different plants (see Material and methods). Our simulated data revealed a correlation between phylogenetic distance of medicinal floras and cultural distance which was highly significant (r = 0.82, p < 0.001 table 1), showing that the approach used here is capable of recovering signal of vertical transmission of medicinal floras, and that absence of this signal in our real dataset is not a methodological artefact.

4. Conclusion

Intriguing patterns are emerging from the increasing numbers of studies using phylogenetic approaches to characterize cultural evolution [4,9–14]. While many studies have identified cultural traits which are predominantly vertically or horizontally inherited, we develop a method able to elucidate the effects of the environment while acknowledging both of these evolutionary processes. Using this method, we demonstrate that ethnic groups resemble each other in medicinal plant use simply because they exist in similar floristic environments, regardless of whether they are geographically proximate or share common ancestors. Future work could focus on evolutionary change in other aspects of traditional knowledge which might be influenced by environment [53], providing insights into the interplay between the environment and traditional knowledge. Further work could also investigate the rates of this evolutionary change [54–56] and the coevolution between traditional knowledge and different aspects of culture [7,57] in different environmental settings, shedding more light into the tempo and mode of the of the interaction between culture and the environment.


Phytochemical Investigations and In Vitro Bioactivity Screening on Melia azedarach L. Leaves Extract from Nepal

Melia azedarach is a common tree used in the traditional medicine of Nepal. In this work, leaves were considered as source of bioactive constituents and composition of methanol extract was evaluated and compared with starting plant material. Flavonoid glycosides and limonoids were identified and quantified by HPLC-DAD-MS n approaches in dried leaves and methanolic extract, while HPLC-APCI-MS n and GC/MS analysis were used to study phytosterol and lipid compositions. β-Sitosterol and rutin were the most abundant constituents. HPLC-APCI-MS n and HPLC-DAD-MS n analysis revealed high levels of phytosterols and flavonoids in methanolic extract accounting 9.6 and 7.5 % on the dried weight, respectively. On the other hand, HPLC/MS n data revealed that limonoid constituents were in minor amount in the extract <0.1 %, compared with leaves (0.7 %) indicating that degradation occurred during extraction or concentration procedures. The methanol extract was subjected to different bioassays, and antioxidant activity was evaluated. Limited inhibitory activity on acetyl and butyryl cholinesterase, as well as on amylase were detected. Moreover, tyrosinase inhibition was significant resulting in 131.57±0.51 mg kojic acid equivalents/g of dried methanol extract, suggesting possible use of this M. azedarach extract in skin hyperpigmentation conditions. Moderate cytotoxic activity, with IC50 of 26.4 μg/mL was observed against human ovarian cancer cell lines (2008 cells). Our findings indicate that the Nepalese M. azedarach leaves can be considered as valuable starting material for the extraction of phenolics and phytosterols, yielding extracts with possible cosmetic and pharmaceutical applications.

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Identify this tree from Nepal - Biology

Basics of Tree ID

Identifying and Classifying organisms is fundamental to Biological Sciences. All living things are divided up into groups. Each individual in the group has similar characteristics. The broadest group is the Kingdom and the most specific group is the species.

The first step in tree identification is knowing that there are always distinguishing characteristics that separate one tree species from another. By examining different tree parts you will be able to confidently identify the different trees around your school. This will require some careful detective work on your part, but it should be fun and easy.

  • TREE TYPE --Deciduous or Conifer? Tree or a shrub? Determining these things starts you off on your way to tree identification.
  • LEAF --Leaves are often the easiest way to identify most trees. Are the leaves arranged in an opposite or alternate pattern?
  • BARK --Bark can be helpful for identifying some types of trees.
  • FRUIT --The wide variety of fruit shapes makes them useful when identifying trees.
  • TWIG --You can actually tell a lot just by looking at the twig.
  • FORM --The way a tree grows can tell you a great deal about a tree.

Some of the text and photos provided in this section courtesy Rodney Nice, Student Presenter, Fall 2000 (Check out Rodney's Recipe for sassafras tea!)


Root Growth and Anatomy

Figure 2. A longitudinal view of the root reveals the zones of cell division, elongation, and maturation. Cell division occurs in the apical meristem.

Root growth begins with seed germination. When the plant embryo emerges from the seed, the radicle of the embryo forms the root system. The tip of the root is protected by the root cap, a structure exclusive to roots and unlike any other plant structure. The root cap is continuously replaced because it gets damaged easily as the root pushes through soil. The root tip can be divided into three zones: a zone of cell division, a zone of elongation, and a zone of maturation and differentiation (Figure 2). The zone of cell division is closest to the root tip it is made up of the actively dividing cells of the root meristem. The zone of elongation is where the newly formed cells increase in length, thereby lengthening the root. Beginning at the first root hair is the zone of cell maturation where the root cells begin to differentiate into special cell types. All three zones are in the first centimeter or so of the root tip.

The root has an outer layer of cells called the epidermis, which surrounds areas of ground tissue and vascular tissue. The epidermis provides protection and helps in absorption. Root hairs, which are extensions of root epidermal cells, increase the surface area of the root, greatly contributing to the absorption of water and minerals.

Figure 3. Staining reveals different cell types in this light micrograph of a wheat (Triticum) root cross section. Sclerenchyma cells of the exodermis and xylem cells stain red, and phloem cells stain blue. Other cell types stain black. The stele, or vascular tissue, is the area inside endodermis (indicated by a green ring). Root hairs are visible outside the epidermis. (credit: scale-bar data from Matt Russell)

Inside the root, the ground tissue forms two regions: the cortex and the pith (Figure 3). Compared to stems, roots have lots of cortex and little pith. Both regions include cells that store photosynthetic products. The cortex is between the epidermis and the vascular tissue, whereas the pith lies between the vascular tissue and the center of the root.

The vascular tissue in the root is arranged in the inner portion of the root, which is called the stele (Figure 4). A layer of cells known as the endodermis separates the stele from the ground tissue in the outer portion of the root. The endodermis is exclusive to roots, and serves as a checkpoint for materials entering the root’s vascular system. A waxy substance called suberin is present on the walls of the endodermal cells. This waxy region, known as the Casparian strip, forces water and solutes to cross the plasma membranes of endodermal cells instead of slipping between the cells. This ensures that only materials required by the root pass through the endodermis, while toxic substances and pathogens are generally excluded. The outermost cell layer of the root’s vascular tissue is the pericycle, an area that can give rise to lateral roots. In dicot roots, the xylem and phloem of the stele are arranged alternately in an X shape, whereas in monocot roots, the vascular tissue is arranged in a ring around the pith.

Figure 4. In (left) typical dicots, the vascular tissue forms an X shape in the center of the root. In (right) typical monocots, the phloem cells and the larger xylem cells form a characteristic ring around the central pith.


Dendrochronology: What Tree Rings Tell Us About Past and Present

Dendrochronology is the study of data from tree ring growth. Due to the sweeping and diverse applications of this data, specialists can come from many academic disciplines. There are no degrees in dendrochronology because though it is useful across the board, the method itself is fairly limited. Most people who enter into studying tree rings typically come from one of several disciplines:

  • Archaeology - for the purpose of dating materials and artefacts made from wood. When used in conjunction with other methods, tree rings can be used to plot events.
  • Chemists - Tree rings are the method by which radiocarbon dates are calibrated.
  • Climate Science - particularly in the field of palaeoclimatology where we can learn about the environmental conditions of the past, locally or globally, based on what the tree rings are telling us. By extension, this can also teach us about climate change in the future
  • Dendrology - which also includes forestry management and conservation. Dendrologists are tree scientists and examine all aspects of trees (1). Tree rings can tell them about the present local climate

Though dendrochronology also has uses for art historians, medieval studies graduates, classicists, ancient and historians due to the necessity to date some of the materials that the fields will be handling in their research projects. Typically, a bachelor's degree in any of the above disciplines are enough to study the data that comes out of dendrochronology.


Acknowledgements

We thank Junzhen Wang and Ayi Shen for their efforts on the seed harvest and phenotyping. We thank Mengqi Ding, Cheng Cheng, Tianbin Tan, Cheng Chen, Guoying Gao, Xiaofang Wu, and Xiaoling Lu for their efforts on the preparation of plant samples and molecular experiments. We thank Ivan Kreft, Oksana Sytar, Christian Zewen, Grażyna Podolska, Galina Suvorova, Jianping Cheng, Jingjun Ruan, Chaoxia Sun, and the National Crop Genebank of China for providing most of the Tartary buckwheat germplasm resources.

Review history

The review history is available as Additional file 3.

Peer review information

Tim Sands was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.


Methods

Mitogenome dataset

In order to clarify the phylogeny of haplogroups M, N and R in South Asia, we focused our study on the lineages with recognized or potential likely origin in the Subcontinent, belonging to macrohaplogroups M (M2, M3, M4’67, M5, M6, M13’46’61, M31, M32’56, M33, M34’57, M35, M36, M39, M40, M41, M42b, M44, M48, M49, M50, M52, M53, M58, M62), R (R5, R6, R7, R8, R30 and R31) and N (N1’5). We also studied U2 (excluding U2e due to its West Eurasian origin) in a complementary analysis. We obtained 381 whole-mtDNA sequences from the 1KGP [33] (although we note that these were collected from caste families from India and lack tribal groups) and 51 from the HGDP [34]. In addition, we generated 13 new sequences (accession numbers: KY686204 -KY686216) belonging to the aforementioned haplogroups from Southeast Asia: seven from Myanmar, one from Vietnam, one from Thailand and four from Indonesia. We combined these with other published data from South Asia and neighbouring areas, including a total of 1478 samples (Additional file 1: Table S1). The additional sequences increased substantially the sample size particularly in the West of the Indian Subcontinent, necessitating a re-evaluation of previously inferred phylogeographic patterns [2, 35].

In order to discern migrations into the Subcontinent at different time periods, we also performed a complementary analysis of several “non-autochthonous” N lineages present in South Asia (H2b, H7b, H13, H15a, H29, HV, I1, J1b, J1d, K1a, K2a, N1a, R0a, R1a, R2, T1a, T2, U1, U7, V2a, W and X2—all subclades of West Eurasian haplogroups), amounting to a total of 635 mtDNA sequences (Additional file 1: Table S2). We assigned haplogroups using HaploGrep [36], in accordance with the nomenclature in PhyloTree (Build 17, February 2016) [37].

Phylogenetic reconstruction and statistical analyses of mtDNA

We reconstructed the mitogenome phylogenetic tree manually, based on a preliminary reduced-median network analysis [38] with Network v.4.611, checked considering the frequency of each mutation [30] and the nomenclature of PhyloTree (Build 17) [37]. We estimated coalescence ages within haplogroups M and N using both the ρ statistic [39] and maximum likelihood (ML). We calculated ρ estimates with standard errors estimated as in Saillard et al. [40] using a synonymous clock of one substitution in every 7884 years and a mitogenome clock of one substitution every 3624 years further corrected for purifying selection [30]. We assessed ML estimations using PAML 4 and the same mitogenome clock assuming the REV mutation model with gamma-distributed rates (discrete distribution of 32 categories) and two partitions, in order to distinguish hypervariable segments I and II (HVS–I and HVS–II) from the rest of the molecule. We performed runs both assuming and not assuming a molecular clock, in order to perform likelihood ratio tests (LRT) [41].

Since haplogroup M displays a peculiar phylogeographic pattern in South Asia [2], we additionally estimated node ages in different sub-regions of the Subcontinent (west, south, central and east) with two different approaches: (1) considering all samples from a given region, regardless of the putative geographical origin of the clade and (2) considering the most probable origin of each major haplogroup (by considering branching structure, number of main branches, and centre of gravity) and including only basal lineages of each region [2]. To evaluate the effective population size (N e) of haplogroup M in each region, we computed Bayesian Skyline Plots (BSPs) [42] using BEAST 1.8.0 [43]. Although haplogroups do not equate to populations, BSPs applied to specific lineages can provide insights into the size variations of the populations that include them [44,45,46,47]. We used a relaxed molecular clock (lognormal in distribution across branches and uncorrected between them), a two-parameter nucleotide evolution model and a mutation rate of 2.514 x 10 -8 mutations per site per year [48].

GW dataset and analysis

We filtered a dataset comprising 1440 samples with 500,123 SNPs, combining data from the 1KGP and 8 independent studies (Additional file 1: Table S3) for linkage disequilibrium (LD) using PLINK v1.07 [49] (r2 > 0.25, with a window size of 100 SNPs and step size of 1), yielding a subset containing 164,149 SNPs. We subjected these to principal component analysis (PCA) using the standard PCA tool provided in EIGENSOFT v6.0.1 [50], with which we calculated the first 10 principal components (PCs), from which we calculated the fraction of variance. We included three additional 1KGP populations—Han Chinese from Beijing, China (CHB), Tuscans from Italy (TSI) and Yoruba from Nigeria (YRI)—for ADMIXTURE v1.23 [51] and sNMF [52] analyses for cross-checking. We performed runs for values of K between 2 and 10, with 5-fold cross-validation in ADMIXTURE, and complementary analyses including Yamnaya aDNA samples [53]. The filtered datasets used (r2 > 0.25, window size of 100 SNPs and step size of 1) included 66,245 SNPs, for ADMIXTURE analysis, and 64,926 SNPs for the PCA.

In order to assess potential sex-biased gene flow into the region, we compared uniparental (mtDNA and Y-chromosome) and autosomal ancestry in the five 1KGP South Asian populations: Bengali from Bangladesh (BEB), Gujarati Indian from Houston (GIH), Indian Telugu from the UK (ITU), Punjabi from Lahore, Pakistan (PJL) and Sri Lankan Tamil from the UK (STU). For the autosomal ancestry variation, we considered the mean of each component for the highest likelihood value. The putative origin of the uniparental lineages present in the populations is shown in Additional file 1: Table S4. Y-chromosome phylogeny was based on Yfull tree v4.10 (https://www.yfull.com/tree/) [54]. We considered as South Asian the Y-chromosome lineages that most likely entered the Subcontinent before the Last Glacial Maximum (LGM): H [55,56,57], K2a1* [58] (this attribution on the basis of the early-branching lineage, and therefore uncertain, but only concerns a single sample and does not affect the results in any way), and C5 [58]. Y-chromosome haplogroups G, J, L1, L3, Q, R1 and R2 seem to have entered South Asia more recently in the early to mid-Holocene from a West Eurasian source [17,56,57,58,, 55–59]. C(xC5), O and N probably had a Holocene Eastern origin [55, 58, 60, 61].


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