10.1: Plant Diversity Imbalance - Biology

10.1: Plant Diversity Imbalance - Biology

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The diversity of flowering plants (the angiosperms) dwarfs the number of species of their closest evolutionary relatives (Figure 10.1). There are more than 260,000 species of angiosperms (that we know; more are added every day). The clade originated more than 140 million years ago (Bell et al. 2005), and all of these species have formed since then. One can contrast the diversity of angiosperms with the diversity of other groups that originated at around the same time. For example, gymnosperms, which are as old as angiosperms, include only around 1000 species, and may even represent more than one clade. The diversity of angiosperms also dwarfs the diversity of familiar vertebrate groups of similar age (e.g. squamates - snakes and lizards - which diverged from their sister taxon, the tuatara, some 250 mya or more Hedges et al. 2006, include fewer than 8000 species).

The evolutionary rise of angiosperm diversity puzzled Darwin over his career, and the issues surrounding angiosperm diversification are often referred to as “Darwin’s abominable mystery” in the scientific literature (e.g. Davies et al. 2004). The main mystery is the tremendous variation in numbers of species across plant clades (see Figure 10.1). This variation even applies within angiosperms, where some clades are much more diverse than others.

At a global scale, the number of species in a clade can change only via two processes: speciation and extinction. This means that we must look to speciation and extinction rates – and how they vary through time and across clades – to explain phenomena like the extraordinary diversity of Angiosperms. It is to this topic that we turn in the next few chapters. Since Darwin’s time, we have learned a lot about the evolutionary processes that led to the diversity of angiosperms that we see today. These data provide an incredible window into the causes and effects of speciation and extinction over macroevolutionary time scales.

Figure 10.1. Diversity of major groups of embryophytes (land plants); bar areas are proportional to species diversity of each clade. Angiosperms, including some 250,000 species, comprise more than 90% of species of land plants. Figure inspired by Crepet and Niklas (2009). Image by the author, can be reused under a CC-BY-4.0 license.

Comparative methods can be applied to understand patterns of species richness by estimating speciation and extinction rates, both across clades and through time. In this chapter, I will introduce birth-death models, by far the most common model for understanding diversification in a comparative framework. I will discuss the mathematics of birth-death models and how these models relate to the shapes of phylogenetic trees. I will describe how to simulate phylogenetic trees under a birth-death model. Finally, I will discuss tree balance and lineage-through-time plots, two common ways to measure the shapes of phylogenetic trees.

An imbalance in nutrients threatens plant biodiversity

An unnatural balance of nutrients threatens biodiversity in a survival of the fittest scenario, according to the results of a world-first global experiment published in the journal Nature. Professor Jennifer Firn, from QUT's Science and Engineering Faculty, is part of a global network of researchers who have tested the impact increased nutrient levels is having on grasslands across six continents.

The article is titled "Addition of multiple limiting resources reduces grassland diversity" and was led by Professor Stan Harpole from UFZ and iDIV, Germany.

"As part of the Nutrient Network, researchers tested the Charles Darwin 'entangled bank' observation which is used to explain how species can coexist even if they require the same limiting resources.

"This theory explains the mechanism of how a number of species should be competing for resources when they are actually coexisting because of the subtle differences in their resource needs.

"But what we found was that if you change the limiting resources and add an abundance of resources such as nutrients like phosphorus, nitrogen and potassium, it will lead to a favouring of some species over others because competition is then shifted above ground for light.

"This will in turn evoke competition between species, leading to one species dominating the land area."

The experiment was conducted across 45 grassland sites spanning the multi-continent Nutrient Network.

Professor Firn said the human influence on the nutrient cycle through greater globalisation, was having a damaging effect on ecosystem biodiversity.

"The loss of diversity was not driven by the addition of any single added resource for example nitrogen or potassium, we found greatest diversity loss occurred with the addition of a combination of two or more resources," she said.

"Simply put, the more nutrients, the less biodiversity."

She said many of the ecosystem functions that humans need to survive were provided by richly diverse ecosystems, such as oxygen production, water filtration, nutrient cycling, pollination, and carbon sequestration.

"The irreplaceable loss of native biodiversity is accelerating at an alarming rate globally," she said.

"What this research does is provide tangible evidence that global change is driving environmental conditions beyond our planetary boundaries."

The Nutrient Network is the only collaborations of its kinds in which individual researchers have set up the same experiments at sites around the world. It is coordinated through the US-based National Science Foundation's funding to biologists Prof. Elizabeth Borer and Prof. Eric Seabloom of the University of Minnesota.


A major implication of natural selection is that species from different parts of the world will vary in their efficiency in converting resources into offspring for a given type of environment. This insight, articulated by Darwin, is usually overlooked in more recent studies of invasion biology that are often based on the more modern Eltonian perspective of imbalanced ecosystems. We formulate a renewed Darwinian framework for invasion biology, the evolutionary imbalance hypothesis (EIH), based only on the action of natural selection in historically isolated populations operating within a global network of repeated environments. This framework predicts that successful invaders are more likely to come from biotic regions of high genetic potential (with independent lineages of large population size), experiencing a given environment for many generations and under strong competition from other lineages.



We test the predictive power of this framework by examining disparities in recent species exchanges between global biotic regions, including patterns of plant invasions across temperate regions and exchanges of aquatic fauna as a result of modern canal building.


Our framework successfully predicts global invasion patterns using phylogenetic diversity of the world's biotic regions as a proxy that reflects their genetic potential, historical stability and competitive intensity, in line with the Darwinian expectation. Floristic regions of higher phylogenetic diversity are more likely to be source areas of invasive plants, and regions of lower phylogenetic diversity are more likely to be invaded. Similar patterns are evident for formerly isolated marine or freshwater assemblages that have been connected via canals.

Main conclusions

We advocate an approach to understanding modern species invasions that recognizes the potential significance of both the original Darwinian explanation and the more modern view that emphasizes novel ecological or evolutionary mechanisms arising in the introduced range. Moreover, if biological invasions are a natural outcome of Darwinian evolution in an increasingly connected world, then invasive species should continue to displace native species and drive widespread shifts in the functioning of ecosystems.


Study streams

We commenced our study with 43 headwater stream sites located in 43 regions from 26 countries (Fig. 3), but three streams were heavily disturbed by freezing or floods and so were excluded from analysis the excluded streams were in Norway, Maryland (United States), and Rio Grande do Sul (Brazil). Streams were similar in size (orders 1 to 3) and physical habitat (alternating riffles and pools), mostly with dense canopy cover and rocky substrate, and each was representative of its region in terms of riparian vegetation. Mean water temperature during the experiment (measured with data loggers every 1 hour in most cases, otherwise measured several times during the experiment) varied between 1.8° and 28.3°C pH varied between 3.9 and 8.3 (being circumneutral in 80% of streams) dissolved oxygen was close to 100% saturation 70% of streams had low concentrations of nutrients [nitrate (N-NO3) (<700 μg liter −1 ), ammonium (N-NH4) (<65 μg liter −1 ), and phosphate (P-PO4) (< 35 μg liter −1 )] and riparian plant diversity varied from streams with fewer than 10 species to others with more than 40 species (table S2 and fig. S1).

Litter mixtures

We used three low-diversity and three high-diversity litter mixtures (I to III and IV to VI), which corresponded to species of the same plant family (or genus) or to different families, respectively (Fig. 2). Families were chosen to represent different trait syndromes and worldwide distributions: (i) Betulaceae (Alnus), with higher-quality litter and wide distribution (ii) Moraceae (Ficus), with intermediate-quality litter and tropical distribution and (iii) Fagaceae, with lower-quality litter and northern temperate distribution (Fig. 2) (29). The species selected were Alnus acuminata Kunth., A. glutinosa (L.) Gaertn., A. incana (L.) Moench, Ficus insipida Willd, Ficus natalensis Hochst., Ficus dulciaria Dugand, Fagus sylvatica L., Quercus prinus Willd., and Castanea sativa Mill. Given that using all possible high-diversity combinations was unfeasible, we randomly chose one species from each family to be included in each of the three high-diversity mixtures, without replacement (i.e., each species was present in only one high-diversity and one low-diversity mixture). We calculated the phylogenetic distance of each of the six mixtures (and of all other possible high-diversity combinations) using the “” tool in Python 2.7 based on a phylogenetic tree of angiosperms that was constructed for a previous study (14). Phylogenetic distance was 237 ± 24 (mean ± SD) in low-diversity mixtures and 357 ± 5 in high-diversity mixtures (table S9).

We collected litter with no visible signs of herbivory or decomposition, from the riparian forest floor or using vertical traps. Different species were collected in different regions (fig. S3), as there was a trade-off between origin and the comprehensiveness of the pool of species and traits. We sacrificed the former despite a possible home-field advantage (HFA) effect (42), because there is little evidence that HFA occurs for instream decomposition (43, 44), and HFA generally explains much lower variability in decomposition than litter traits and climate (42). In addition, we discarded the use of artificial substrates that would have removed any HFA effect (e.g., cotton strips) because they would not allow the different diversity treatments required to test our hypotheses and because they do not account for detritivore feeding activity (33). Litter was air-dried in laboratories and distributed among partners.


In each region, we selected a permanent stream reach with length approximately 10 times the wetted stream width, within which we chose five consecutive pools in which to conduct the experiment. The experiment was run during stable flow conditions, at the time of the year (2017–2019) with greatest litter inputs to the stream (e.g., autumn in northern temperate regions and dry season in many tropical regions). We enclosed litter of each mixture (I to VI) within coarse-mesh (5 mm) and fine-mesh (0.4 mm) litterbags (approximately 1 g per species, 3 g in total, weighed precisely), with five replicates per treatment (i.e., combination of mixture and mesh type), resulting in 60 litterbags per region and 2580 in total. Despite some potential drawbacks of the litterbag method, it is by far the most widely used method to quantify decomposition in streams, as it resembles the decomposition of litter in depositional zones and allows size-selective exclusion of detritivores (26).

We placed one replicate litterbag per treatment in each pool, with coarse- and fine-mesh litterbags paired, and anchored them to the substrate using steel rods and stones. We retrieved the litterbags after 23 to 46 days, depending on the water temperature in each stream (fig. S1), thereby halting the decomposition process at a comparable stage (which was 59 and 27% for coarse- and fine-mesh litterbags, respectively, for the fastest decomposing species, A. incana, and 32 and 17% for mixtures fig. S2). Upon retrieval, litterbags were enclosed individually in ziplock bags, transported to the laboratory on ice, and subsequently rinsed using filtered stream water to remove attached sediment and invertebrates. Litter was sorted into species and oven-dried (70°C, 72 hours), and a subsample was weighed, incinerated (500°C, 4 hours), and reweighed to estimate final ash-free dry mass (AFDM). LML due to leaching and drying was estimated for each species in the laboratory, and multiple litter traits were examined for each species as detailed by López-Rojo et al. (36).

Data analysis

We quantified litter decomposition rate as the proportion of LML per degree day for each species within a mixture and in total for each mixture (assuming linear decay), separately for coarse- and fine-mesh litterbags. This measure, which accounted for differences in temperature across regions, was calculated as follows: LML = [initial AFDM (g) − final AFDM (g)]/initial AFDM (g), with initial AFDM corrected by leaching, drying, and ash content (i.e., multiplied by the proportion of litter mass remaining after leaching and AFDM calculation, which ranged between 0.59 and 0.85). To assess species-specific patterns, we estimated the litter functional diversity effect on decomposition (LDED for each species and mesh type) as the difference between its LML in the high-diversity mixture and the low-diversity mixture located in the same pool habitat (i.e., there were five replicate values of LDED per species and mesh type Fig. 2).

We could not calculate an LDED to assess whole mixture patterns (i.e., total LML of all species in the mixture) therefore, we used different modeling approaches for species-specific and total decomposition in mixtures. We examined the latitudinal variation of species-specific LDEDs through linear mixed-effects (LME) models (45) [lme function and restricted maximum likelihood method, nlme R package (46)] in which latitude and mesh were fixed effects (fitted as an interaction), and replicates were a random effect nested within region. We ran one model for each species and an overall model where species was included as a random factor to assess patterns in the mean LDED. Data exploration with Cleveland dot plots and boxplots revealed no outliers (47), and their absence was confirmed with Cook’s distances after fitting the models. Models included the variance function structure varIdent, which allowed different variances for each mesh (for individual species models) or mesh and species (for the overall model) the need for this term was identified in initial data exploration and confirmed by comparison of the Akaike information criterion (AIC) of models with and without this component (45). The influence of each species to the overall model was examined with Cook’s distances, which indicated that results were not driven by particular species (table S4). For whole mixtures, we used an LME model where total LML in mixtures was the response variable, litter functional diversity and latitude were fixed effects (fitted as an interaction), treatment (I to VI) was a random effect, and replicates were nested within treatment.

We explored how the LDED varied across biomes (32) through LME models, for coarse- and fine-mesh litterbags separately, with biome as a fixed factor and region as a random factor, and using an aggregated dataset (i.e., average values of five replicates per treatment). We used linear models (lm function) and a forward model selection procedure based on AIC (step function) on the aggregated dataset to assess the importance of four climatic variables (extracted from (48) and four stream environmental variables measured in situ (table S2), which showed variance inflation factors ranging from 1.27 to 2.40. Last, we examined the influence of multiple litter traits (table S5) on the LDED using again linear models and a forward model selection procedure based on AIC.


Previous studies carried out on diverse collections of wild and cultivated carrots suggested there was no distinctive genetic structure within neither wild nor cultivated (western and eastern) carrot [3, 8, 9, 11, 13, 16, 20]. Nonetheless, the above-mentioned studies were carried out on large sets of very diverse carrot germplasm. Because of the apparent and very distinctive genetic differences between wild and cultivated gene pools, they might not have been able to detect the structure of genetic diversity present within the group of western OP cultivars. Historically, the first of the market classes of the western cultivated carrot were selected in the eighteenth century from groups of cultivars showing similar storage root morphologies [21]. As such, the cultivars classified to one market class might have been of relatively close kin. However, as previously reported by Iorizzo et al. [1], no significant bottleneck was observed in the cultivated carrot, pointing at a possible continuous gene flow between the wild and the cultivated pools, and very likely also among different cultivars. Conventional selection based on the plant morphology, leading to the development of the existing types of carrot cultivars showing distinct morphological and agronomic characteristics, apparently allowed retention of significant amounts of genetic heterogeneity within OP cultivars.

The codominant DcS-ILP marker system exploited in the present study might reveal genetic variability which arose more recently as DcSto MITEs show extreme insertional polymorphism within the carrot genome [19, 22]. Possibly, their recent mobilisation could have led to the genetic diversification within the western carrot gene pool. Despite many advantages, high throughput molecular marker systems, such as SNPs or DArTs are not able to detect transposable element (TE) insertion-derived variability. The resolving power of the DcS-ILP panel was previously demonstrated on the collection of 23 OP cultivars of western type carrot [18]. The panel of high quality SNPs bears advantages of cost-efficient throughput sequencing-derived markers but is reduced to ca. 2300 loci evenly-distributed across the genome and referred to the high-quality genome assembly [1], thus providing time- and computing efficiency when exploited for the evaluation of genetic structure within the larger datasets. Both panels of markers can easily be extended by additional loci to gain extra biological information or to possibly modify the resolution of population structure.

The results of the AMOVA, together with high values of HO, on both cultivar and market class level indicated a significantly higher level of intra-cultivar genetic diversity, mainly contributing to the overall genetic diversity observed in the investigated collection of cultivars. This observation is in accordance with previous studies of Maksylewicz and Baranski [10], as they indicated that almost two third of the of total variation observed in highly diverse collection of carrot cultivars and landraces was attributed to intra-population variation. The values of inbreeding coefficients in the collections of both advanced cultivars and landraces in the studies carried out by Baranski et al. [9] and Maksylewicz and Baranski [10] indicated the excess of homozygous loci that could suggest the repeated selfing during breeding programs aimed at the production of uniform, advanced cultivars. However, using a much more robust set of polymorphisms we did not observe positive inbreeding coefficients in our collection of OP cultivars.

Cultivars classified as Chantenay, Amsterdam and Paris Market types showed lower gene diversity (measured as HE) among the 11 predefined market classes, whereas cultivars classified to the Berlikum, St. Valery and Imperator types were among the most heterogenous. STRUCTURE clustering led to the decrease in the most probable number of groups from 11 predefined market classes to the most probable three to five or seven clusters. Non-model DAPC grouping also indicated the lesser number of relatively homogenous groups in the examined collection of cultivars. The choice of the most probable number of clusters was more ambiguous for DcS-ILP markers as the differences in the ΔK value were relatively small, but generally the increase of the number of defined clusters resulted in lower fraction of the unassigned cultivars together with the increase of the average membership coefficient (Q) within the clusters. This tendency was reversed in the case of SNP genotyping data. The more clusters were extracted, the lower mean values of Q within the groups were observed. Nonetheless, most of the cultivars belonging to the Amsterdam and Chantenay types were always clearly separated from other varieties and characterized by the highest values of within-group Q together with the lowest within-group HE. According to the classification of carrot market types proposed by Banga [14] both the Amsterdam and the Chantenay types represent the ‘Horn’ group that comprises a vast selection of high-quality carrot cultivars subdivided to at least eight market classes (Fig. 8). The Amsterdam market type refers to forcing carrot cultivars grown under covers or for early production in the open. The use of Amsterdam cultivars was originally limited to an early production under covers. Breeders were seeking plant material characterised by a high yield and considerable length, together with the vigour being the key feature in forcing varieties. In the nineteenth century only few varieties could meet the expectations, with Utrecht Forcing among them. The modern Amsterdam OP cultivars are believed to be direct descendants of the Utrecht Forcing [14].

Schematic representation of the origin of main types of Western carrot as proposed by Banga [14]. Solid arrows show direction of the development of new cultivar types. Punctuated arrows indicate possible origin of the particular type. Underlined names indicate the types of cultivars investigated in the present study. Colours of the boxes represent particular market types or groups of market types clustered using DAPC method for both DcS-ILP and SNP data (see Fig. 7)

To date, there is no evidence pointing out that any other breeding material was used to develop Amsterdam cultivars. It is in accordance with our results of STRUCTURE and DAPC clustering, indicating a relatively strong distinctiveness of Amsterdam carrots from other market types, possibly allowing preservation of its specific agricultural characteristics. The admixed nature of Amsterdamska cv. possibly reflects the use of Nantes breeding material in the course of the cultivar development. The Chantenay market type comprises cultivars developed for the production between half-summer and late winter carrots. This type is considered as a deviation from main types representing ‘Late Half Long Horn’ group of market types and was developed as a parallel selection to Guerande type and are believed to originate from la race de Hollande cv [14].. However, the majority of Chantenay cultivars was grouped in one clearly distinctive cluster with very high membership values. Regardless of the molecular marker genotyping system, two cultivars, i.e. Chantenay Long Type and Criolla, were characterized by high levels of admixture. Interestingly, in both cultivars the major genetic components were derived from the market classes with shared ancestor according to the classification proposed by Banga [14]. The two major genetic components of Chantenay Long Type cultivar pointed towards the cross between breeding materials belonging to the Danvers and Imperator types. Since Chantenay Long Type originated in the U.S., the breeding material of Danvers type could be introduced in the course of the cultivar development, especially because this market type originated in the U.S. in 1870s, and is still used for bunching [14]. The relatively high proportion of genetic components originating from Imperator might be the effect of the crosses between Chantenay and Imperator aimed at obtaining longer storage root. Similarly, the reason for clustering of Criolla cultivar (originally classified as Chantenay) with cultivars of the Danvers type could be in the use of the most easily accessible parental breeding material originating from North America. It highlights possible discrepancies between the passport data attributing a cultivar to a particular market class relative to its phenotype and its actual pedigree. Ma et al. [13] reported that Chinese orange carrots, sharing many morphological characteristics with western orange carrots, clustered with Chinese red carrots, suggesting that Chinese orange cultivars and landraces could have emerged from original Asian carrot varieties. It shows that similar phenotypic traits can result from selection from different gene pools. Thus, the presented results possibly reveal the actual genetic diversity within the western carrot gene pool, coupled with remarkable intra-cultivar heterogeneity and significant levels of admixture.

10 Major Causes for the Loss of Biodiversity

(1) Destruction of Habitat, (2) Hunting, (3) Exploitation of Selected Species, (4) Habitat Fragmentation, (5) Collection for Zoo and Research, (6) Introduction of Exotic Species, (7) Pollution, (8) Control of Pests and Predators, (9) Natural Calamities, and (10) Other Factors.

Cause #1 Destruction of Habitat:

The natural habitat may be destroyed by man for his settlement, agriculture, mining, industries, highway construction, dam building etc.

As a consequence, the species must either adapt to the changes in the environment, move elsewhere or may succumb to predation, starvation or disease and eventually die. Several rare butterfly species are facing extinction due to habitat destruction in the Western Ghats. Of the 370 butterfly species available in the Ghats, around 70 are at the brink of extinction.

Cause #2 Hunting:

Wild animals are hunted for the commercial utilization of their products such as hides and skin, tusk, fur, meat, pharmaceuticals, cosmetics, perfumes and decoration purposes. In Africa, in recent years 95% of the black rhino population have been exterminated in Africa by poachers for their horn. Today, rhino horn fetches more than $15,000 in the pharmaceutical market.

In the last one decade, over one-third of Africa’s elephants have been killed to collect 3,000 tonnes of ivory. International regulations have, to a great extent, reduced illegal trading and poaching of African Tuskers. In 1987, the Indian Govt. also banned the trade in Indian ivory. The scarlet macaw, once common throughout South America, has been eliminated from most of its range in Central America.

Several species of spotted cats such as ocelot and Jaguar have been jeopardized by the demand for their fur. In 1962, nearly 70,000 whales were slaughtered. However, international trade in whale products is banned now.

In India, rhino is hunted for its horns, tiger for bones and skin, musk deer for musk (medicinal value), elephant for ivory, Gharial and crocodile for skin and jackal for fur trade in Kashmir. One of the most publicized commercial hunts is that on whale. Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) listed 9 Indian animal species which have been severely depleted due to international trade.

These are Fin Whale (Balaenoptera physalus), Himalayan Musk Deer (Moschus moschiferus), Green Turtle (Chelonia mydas), Hawksbill Turtle (Eretmochelya imbricata), Olive Ridley Turtle (Dermochelys olivacea), Salt-water Crocodile (Crocodylus porosus), Desert Monitor Lizard (Varanus griseus), Yellow Monitor Lizard (V. flavesoens) and Bengal Monitor Lizard (V. bengalensis).

Officials of Trade Record Analysis of Flora and Fauna in Commerce (TRAFFIC-India) say poaching of the Indian tiger has risen because of increasing demand from south­east Asian countries and China, where pharmaceutical factories consume the bones of 100 tigers each year. Such demand has decimated the tiger population in China and brought the Russian tiger to the brink of extinction.

As a result, in recent years much of the demand has been met by poachers in India. One kg of tiger bones fetches $ 90 in India and $300 in the international market. Hunting for sport is also a factor for loss of animal biodiversity.

Cause #3 Exploitation of Selected Species:

Exploitation of medicinally important plants has resulted in their disappearance from many of their natural habitat. The pitcher plants, Nepenthes khasiana, Drosera sp., Gnetum sp., Psilotum sp. Isoetes sp. are ruthlessly sought and collected for teaching and laboratory work.

They have already become rare. Medicinal plants like Podophyllum sp., Coptis sp., Aconitum sp., Rouvolfia sp., Saussura lappa, Atropa acuminata, Dioscorea deltoidea etc. are also disappearing rapidly as a consequence of merciless over-collection. Similarly, the natural populations of a number of economically important trees like Pterocarpus santalum, Dysoxylon malabaricum, Santalum album which yield valuable timber are fast dwindling.

In the category of over-exploited plants may also be placed a number of orchids producing world’s most showy flowers. Plants like Paphiopedilum fairieyanum, Cymbidium aloiflium, Aerides crispum etc. are in great demand but their natural populations have almost disappeared.

Today, only nine varieties of wheat occupy more than half of United States wheat fields. Almost 95% of the old strains of wheat grown in Greece before the Second World War (1939-1945) have disappeared. They are replaced by a few new hybrid varieties. Only four varieties provide almost 72% of the entire potato harvest of the United States.

Over 2,000 varieties of apples were under cultivation during the earlier century. Today, three-fourth of entire apple production of France consists of North American varieties of which nearly 70% happens to be the Golden variety. Indonesia has lost nearly 1,500 strains of rice and nearly three fourth of its rice production comes from varieties discussed from a single maternal stock.

Practically all varieties of Sorghum grown in South Africa have disappeared following introduction of high yielding hybrid varieties from Texas. In India, an estimated 50-60 thousand varieties of rice were cultivated before independence, most of which are being dropped in favour of a few high yielding varieties.

All over the world traditional varieties which together constituted a diverse mosaic, are being dropped one by one being replaced by a few high yielding strains. The reduction of genetic diversity among the cultivated species and the disappearance of their wild relatives, drastically limit possibilities of creating new cultivar in the future.

Cause #4 Habitat Fragmentation:

Habitat fragmentation may be defined as an “unnatural detaching or separation of expansive tracts of habitats into spatially segregated fragments” that are too limited to maintain their different species for an infinite future.

This phenomenon was observed as early as 1885 when de Candolle noticed that ‘the break­up of a landmass into smaller units would necessarily lead to the extinction or local extermination of one or more species and the differential preservation of others’.

Habitat fragmentation is one of the most serious causes of erosion of biodiversity. Fragmentation leads to artificially created ‘terrestrial islands’. Such fragments experience microclimatic effects markedly different from those that existed in the large tracks of habitats before fragmentation. Air temperature at the edges of fragments can be significantly higher than that found in the interior light can penetrate deep into the edge, thereby affecting the growth of existing species. Fragmentation promotes the migration and colonization of alien species. Such substantial and continuous colonization, profoundly affect the survival of native species.

The most serious effect of fragmentation is segregation of larger populations of a species into more than one smaller population. There is considerable evidence that the number of species in a fragmented habitat will decrease over time, although the probable rates at which it will happen are variable. In fact, actual data on rain forests show that forest fragments have lower species richness and fewer populations compared with continuous undisturbed forests.

An example of loss of biodiversity as the result of the fragmentation is that of the Western forest of Ecuador, which were largely undisturbed till 1960, where newly constructed network of roads led to rapid human settlements and clearance of much of the forest area, have been fragmented into small patches of one to few square kilometers.

Such a patch, about 0.8 square kilometers in area at Rio Palenque Biological station now contains only about 1,033 plant species many of which are represented by a single specimen only and are endemic to the locality. Before 1960 the intact forest had thousands of species as found in any other tropical regions of the world.

Cause #5 Collection for Zoo and Research:

Animals and plants are collected throughout the world for zoos and biological laboratories for study and research in science and medicine. For example, primates such as monkeys and chimpanzees are sacrificed for research as they have anatomical, genetic and physiological similarities to human beings.

Cause #6 Introduction of Exotic Species:

Any species which is not a natural inhabitant of the locality but is deliberately or accidentally introduced into the system may be designated as an exotic species. Native species are subjected to competition for food and space due to the introduction of exotic species.

There are many instances when introduction of exotic species has caused extensive damage to natural biotic community of the ecosystem. The introduction of Nile perch from north in Lake Victoria, Africa’s largest lake, has driven almost half of the 400 original fish species of the lake to near extinction.

Both Eucalyptus and Casuarina are plants introduced in India from Australia. The remarkably fast growth of these plants has made them valuable source of rough timber. However, these plants appear to be ecologically harmful as they tend to suppress the original species of the locality.

While economically useful plants are deliberately introduced a large number of exotic weeds are transferred from one locality to another accidentally. The wheat imported to India from the USA under PL-480 scheme were contaminated with seeds of Parthenium hysterophorus, the congress grass and Agrostemma githago, the corn cockle.

Both of these plants have spread throughout India as a pernicious weed in wheat fields. Parthenium was first observed growing on a rubbish heap in Pune in 1960. It is an aggressive plant which matures rapidly and produces thousands of seeds. The native grasses and other herbs are crowded out of existence. Water hyacinth, Eichornia crassipes, was introduced in 1914 in West Bengal.

The first appearance of Alligator weed, Alternanthera philexeroides, was reported near Calcutta airport in 1965, while Salvinia molesta was brought in India by an aquarist. These plants grow vigorously and result in the formation of thick mat on the water surface. They impede run off in streams and promote water logged conditions. A number of useful water plants are displaced by these vigorous but useless plants. There is an overall reduction in biodiversity wherever these exotic weeds migrate.

Cause #7 Pollution:

Pollution alters the natural habitat. Water pollution especially injurious to the biotic components of estuary and coastal ecosystems. Toxic wastes entering the water bodies disturb the food chain and so the aquatic ecosystems. Insecticides, pesticides, sulphur and nitrogen oxides, acid rain, ozone depletion and global warming too, affect adversely the plant and animal species.

The impact of coastal pollution is also very important. It is seen that coral reefs are being threatened by pollution from industrialization, oil transport and offshore mining along the coastal areas.

Noise pollution is also the cause of wildlife extinction. This has been evidenced by the study by the Canadian Wildlife Protection Fund. According to a study, Arctic Whales are seen on the verge of extinction as a result of increasing noise of ships, particularly ice-breakers and tankers.

Cause #8 Control of Pests and Predators:

Predator and pest control measures, generally kill predators that are a component of balanced ecosystem and may also indiscriminately kill non-target species.

Cause #9 Natural Calamities:

Natural calamities, such as floods, draught, forest fires, earth-quakes, volcanic eruptions, epidemics etc. sometimes take a heavy toll of plant and animal life. Floods are frequent in moist tropical regions of the world which inundate much of the ground vegetation, trap a large number of animals while leading away soil nutrients. Failure of monsoon in succession for two or three years dries up ground vegetation and as the subsurface water table recedes trees are also affected. With plant life animals also suffer.

Forest fires in densely wooded localities often reduce to ashes a large number of plant and animal species and so do earthquakes. Volcanic eruptions may at times completely destroy plant and animal life in its surrounding areas. Epidemics sometimes destroy large portions of a natural population. In nature such episodes are usually confined to specific plant or animal populations as the pathogen is often specific to particular species or group of species.

Cause #10 Other Factors:

Other Ecological Factors that may also Contribute to the Extinction of Plant and Animal Diversity are as follows:

(a) Distribution range—The smaller the range of distribution, the greater the threat of extinction,

(b) Degree of specialization— The more specialized an organism is, the more vulnerable it is to extinction,

(c) Position of the organism in the food chain—The higher the organism in food chain, the more susceptible it becomes,

(d) Reproductive rate—Large organisms tend to produce fewer off springs at widely intervals.

10.1: Plant Diversity Imbalance - Biology

Key Point: Fewer disruptions to soil allows more diverse soil microbes that provide better soil structure for your plants to grow.

When soils are left undisturbed, abundance and diversity of soil microbes increase, driving improved soil microbiome communities and soil structure. These improvements provide both ecological benefits as well as resiliency to crop stressors, crop quality, and ultimately yield.

Ecologically, these practices improve soil structure, reducing both wind and water erosion of soils, reduce agricultural run-off into watersheds, and aid in soil carbon sequestration. On the farm, as some regenerative agriculture theories suggest, growers adopting reduced or no-till practices may see many changes that will benefit their bottom lines economically while rebuilding their soils for future generations. Changes you will see with reduced or no-till practices include increased water penetration and retention, greater soil nutrient retention and availability to crops, less soil crusting, and increased soil organic matter over time. All of these contribute greatly to crop vigor, resiliency to crop stressors, and ultimately, crop yield. Additionally, there are cost reduction opportunities for growers, including reduced tilling soils, reduced requirements for fertilizers, and more efficient use of water resources. Altogether, reduced or no-till practices are key regenerative agriculture practices that will provide valuable benefits in both the near term as well as rebuilding soils for generations to come.

#2 – Cover Cropping

Key Point: By allowing continual plant and root growth in your soil, you are providing the soil with better nutrients to reinvigorate other plants.

Promoting more continual plant and root growth in soils is also a key to soil health and regenerative agriculture. Cover cropping, as some regenerative agriculture theories state, systems can fix CO2 from the atmosphere, sequestering carbon as organic matter in the soils, feed carbon plant root exudates into the soil that promote soil biology, add nutrients to soils, and reduce soil erosion.

Many crops can be used depending on locations and soil needs. Cover crops can be excellent scavengers of excess nutrients left in the soil after crop harvest. They can incorporate the nutrients into their biomass, store, and then recycle excess nutrients until needed at the beginning of the next planting season. Cover cropping will also reduce potential fertilizer leaching into watersheds and groundwater and help to reduce agricultural run-off. Leguminous cover crops can be used to fix nitrogen from the atmosphere into the soil, reducing the need for nitrogen fertilizers the next season.

In some permanent crop systems, cover crops can be interspersed between rows. Keeping soils covered reduces the risk of possible soil erosion, suppresses weeds, and can even provide pollinator habitat. Cover cropping is a key tool that can help to sequester carbon from the atmosphere into soils, recycle nutrients, reduce the need for synthetic fertilizers, reduce agricultural run-off, and promote better soil biology and structure. This is a key tool that can add value to your bottom line while also regenerating your soils for optimal crop productivity and health.

#3 – Composting

Key Point: Using compost to rebuild depleted soils allows for natural and sustainable growth.

Building soil organic is essential for rebuilding depleted soils. Composted biological materials such as crop residue, food waste, and animal waste to build soil organic matter are crucial in regenerative agriculture. These materials contain carbon, that when incorporated into soils breaks down slowly, building stable organic matter. The conversion into stable organic matter takes time.

Compositing can accelerate the decomposition of these materials, creating compost products that can be more immediately available for soil microbes and plants to utilize. Composting processes can be driven by bacteria, fungi, earthworms, nematodes, and other organisms. In addition to adding carbon/organic matter back into soils, composts provide fertilizer value to your soils and crops in forms that are available over more extended periods than conventional fertilizers.

#4 – Increasing Plant Diversity

Key Point: Historically, areas that grew a variety of plants and crops built healthier soil naturally before large scale farming focused more on specialized areas for the same crops year in and year out. Crop rotation plays a critical role in trying to mimic the natural diversity of native plant balances.

Before modern, large scale, intensive agricultural practices, native plant and soil ecosystems co-evolved naturally to achieve a balance that could support a vast diversity of plants grown in the same soil. These perennial crops built stable organic matter in our soils over millennia. This diversity of plants produces a variety of carbon plant exudates that supply carbon to soil biological organisms, as well as a diversity of contributions to soil nutrient profiles.

With the advent of larger-scale annual monoculture, this diversity disappeared, creating imbalances in our soils. The imbalances led to the need for increasing specific nutrients in the form of fertilizers, the degradation of healthy balanced soil biology, degradation of soil structure, and rapid depletion of soil organic matter. Crop rotation plays a critical role in trying to mimic the natural diversity of native plant balances in a way that mimics in part, some of the original benefits that native plant diversity can bring to the soils. When thinking about crop rotations, it is important to consider how one crop can benefit the next rotation from a nutritional standpoint, but also the diversity within a crop type (ex. warm-season grass crops which could include corn, sorghum, Sudan grass).

Crop rotations adding to the diversity of crops, will add to the diversity of soil microorganisms and create soils that assure crop resiliency and optimum yield over time. This practice of incorporating plant diversity also aids in the development of soil microbiome diversity, key to soil health and regenerative agricultural practices.

# 5 – Organic Annual Cropping

Key Point: By focusing less on industrial-scale production, yearly crops can help organically strengthen the soil.

With the advent of industrial-scale agriculture and mass production of inexpensive fertilizers, many connections with sustainable agriculture fundamentals (such as soil health and biodiversity), fell to the wayside. It has since taken on a secondary role in expanding the agricultural product to meet the growing demands of feeding an ever-increasing world population. While these efforts were, and still are, critical, it has become clear our annual cropping systems must change to regenerate soils at the same time we are meeting these significant global challenges. The previous practices are critical to achieving these goals.

There are also other practices developed over the past 30 years with the organic agricultural movement. Throughout this movement, we have learned that industrial-scale organic annual cropping is possible without compromising yield or quality. Many of these now traditional organic practices can play an integral role in annual cropping, reintroducing practices that rebuild soil health and reduce the requirements they need for synthetic fertilizer pesticides. Although the organic industry is rapidly growing and now estimated to be a $43bil market, only 1% (one percent) of all farmland in the United States is in organic production. Overcoming the challenge of adapting organic practices on farmlands has many problems, both economically and politically. Still, as more and more farms adapt organic growing practices that support the regeneration of soils, this represents a considerable opportunity. Often growers have concerns about the 3-year transition period and how they can remain productive and profitable during this period. PhycoTerra® Organic production helps accelerate crop productivity during this transition, reducing economic risk, while accelerating the benefits of healthy biodiverse soils.

#6 – PhycoTerra ® Soil Microbe Food

Key Point: Our product provides a complex carbon food that feeds and provides a strong foundation for soil health.

While the core of regenerable agriculture is soil health, carbon, microbial abundance, and diversity play a role in the health and sustainability of our soils. Without these, we would be farming in a soil devoid of all the essential benefits that living soil ecosystems provide. While the previous practices are critical, PhycoTerra ® products can assure the maximum benefits of these practices. PhycoTerra ® Soil Microbe Food provides a complex carbon food source that is immediately available to soil microbes upon application to fields.

PhycoTerra® products are based on a unique microalgae strain, isolated from soils. Whether from land or the sea, microalgae serve as a foundational carbon food source, fixing CO2 from the atmosphere, to provide the carbon building blocks of the planet’s living organisms. These products are available as very fine suspension of single-celled microalgae that are pasteurized and have a two-year shelf life.

Composition of PhycoTerra ® Soil Microbe Food

While PhycoTerra ® provides immediately available carbon that is utilized by microbes to increase their abundance in soil, the complex composition of PhycoTerra® Soil Microbe Food also assures a dramatic increase in soil microbial diversity. All the previously described practices have their foundation in sequestering more carbon in oil to drive microbial abundance and diversity. By combining these practices with PhycoTerra®, you can ensure these foundational changes will be optimized to deliver the strongest possible effects on your soil health.

By improving soil health, the resiliency of crops to various stresses is improved. This is important as crops are continually being expected to perform under more and more extreme environmental challenges. Another advantage of healthy soils is with increasing resource limitations with key crop resources, such as water, soils can better retain for more effective crop utilization.

PhycoTerra ® products serve a key in the foundational level of regenerative agriculture, a complex food that increases the abundance and diversity of soil microorganisms. With this in mind, PhycoTerra ® Products will accelerate the rates of changes that each regenerative agricultural practice is designed to provide.

#7 – Animal Integration

Key Point: As some regenerative agriculture theories claim, reintroducing livestock to agricultural crops, instead of keeping them separate, gives natural opportunities for nutrient cycling.

For as long as time, livestock and agricultural crops have co-existed in mutually beneficial relationships. With the increasing industrialization of agriculture, livestock production has separated physically from crop production in forms such as concentrated animal feed operations. These types of operations can result in many challenges, including treatment and disposal of animal wastes, water quality, animal health, and risk of contaminating watersheds and aquifers.

Integrating animal grazing with crop production makes sense in many ways—animal grazing after annual crop harvest aids in the conversion of high carbon residues to low carbon organic manure. Grazing on cover crops can allow more nutrient cycling from crop to soil and carbon sequestration into your soils. This practice will mitigate many of the challenges and risks associated with concentrated animal feeding operations.

It is believed that these benefits to soil health, animal health, and the environment make animal integration a key practice for regenerative agricultural practices.

#8 – Managed Grazing

Key Point: As some regenerative agriculture theories claim, animals help speed forage growth through carefully selected grazing.

In managed grazing, divisions of a forage field are created. The divisions can be created using portable fences. The animals can then be shifted between the divisions periodically depending on the number of animals, the speed of forage growth, and the size of the divisions. Animals are then shifted between divisions periodically to allow recovery and re-growth of a division prior to animal rotation. This practice will reduce soil erosion, improve water penetration, and reduce run-off, while at the same time provide quality livestock nutrition. Additionally, this practice will provide all the benefits of the continual plant and root growth to soil health and sequester carbon to build organic matter in the soil.

Managed rotational grazing is a critical regenerative agriculture practice that will improve soil health, nutrient and carbon cycling, grazing crop quality, animal health, and water retention while reducing soil erosion and run-off.

#9 – Silvopasture

Key Point: As some regenerative agriculture theories claim, livestock grazing and tree growth create opportunities for accelerated cycling of nutrients.

Silvopasture is the integration of livestock grazing and trees grown on the same land. Essentially, it establishes grazing within a managed tree product operation. Silvopasture creates additional benefits beyond traditionally managed grazing though this practice creates additional revenues and cost reductions for the tree operations. Native perennial forage crops are often planted between the trees. Animals obtain nutrition from the forage crops and accelerate the cycling of nutrients and carbon to the soils. This practice can also provide shade in hot summer and protection from wind and elements to the animals. The tree operation benefits nutritionally from the manures, from improved soil health and from the additional cash flow of the integrated livestock operation.

This synergistic regenerative agriculture practice improves soil health, provides animal and plant nutrition, and provides an additional revenue stream for tree operations.

#10 – Agroforestry

Key Point: Cropped in between trees, agroforestry allows for a wider variety of crops to be yielded during a season.

While agroforestry includes silvopasture, the practice employs a broader array of tools with the goal of altering large agriculture landscapes in ways that provide broad environmental, social, and economic impacts. In addition to silvopasture, agroforestry incorporates cropping between the rows of trees, forest farming, riparian forest buffers between crops fields watersheds, and windbreaks.

When combined in a deliberate planned and managed fashion, these practices increase plant diversity, soil health, reduce agricultural run-off, guard against soil erosion, and provide habitat for native flora and fauna to thrive. Economically these practices provide additional revenue streams by employing practices such as forest farming, growing a second crop between rows of a tree crop, utilizing wildlife that thrives in riparian forest buffers, and allows for the integration of grazing with tree operations.


Understanding the relationship between genotype and phenotype is a key issue in life sciences with hugely important implications in biomedical R&D, healthcare and agriculture. Innate genetic variability is both the source and consequence of selection in populations of humans, crops and animals. There is a fine balance between the variability in DNA sequences and the evolutionary constraints for conservation of the original state or fixation of a new variant with a selective advantage. Most genetic variants occur as single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels). In eukaryotic genomes, distribution of variants and the rate of spontaneous mutations are not uniform across individual sites 1,2 . Instead, they depend on functional impact of variants on either the protein structure or the RNA structure in the regulatory regions affecting transcription. Thus, a SNP causing a premature stop codon will truncate a protein sequence, which may be phenotypically significant. Not surprisingly, genetic variability in protein coding regions is two to three times lower than in intergenic regions 2,3,4,5 . Similarly, promoter regions (and especially the sequences within transcription factor binding sites, TFBS) are less prone to SNPs and indels than intergenic regions, with the levels of sequence variation within and around TFBSs inferred from their position weight matrix 6 . Genetic variability can be also assessed from an evolutionary point of view, using a combination of phylogenetic and population genetic techniques 7 . Thus, mutational “hot” and “cold” spots were discovered by analysis of a population of 34 E. coli strains 7 . A lower mutation rate was observed in highly expressed genes and in those undergoing stronger purifying selection that reduces retention of deleterious mutations 7 . In another publication, mutational hotspots were linked to the regions of open chromatin in mammalian genomes 8 . Both in normal and cancerous tissues, mutation rates were found to be related to epigenetic features, such as DNA methylation and nucleosome occupancy 9,10 , with disproportionately higher numbers of SNPs occurring in variably methylated DNA regions 11 . Nucleotide composition was also found to be an important factor in SNP distribution 12,13,14,15 , with a quadratic dependence of SNP density on GC content 12,13 . Methylation of cytosines in high-GC regions results in locally increased SNP density 16 . High SNP density is also characteristic for low-GC regions, typically packed into heterochromatin and frequently referred to as “gene deserts” 8 . On the finer scale, 5′ and 3′ untranslated regions of protein-encoding genes (UTRs) feature conserved regions, as revealed by analysis of patterns of sequence conservation in yeast and mammals 17 . In some cases, SNP density in these regions is even lower than in the corresponding coding regions 18 . Conserved areas of UTRs contain binding sites for proteins or antisense RNAs that modulate transport, RNA stability, cellular localization, expression level and translation 17 . Finally, the same nucleotide positions tend to be conserved both between organisms and within organisms, as was established by cross-species analysis of SNP distribution in functionally important regions in mice and men 19 .

Most studies on genomic variability have been conducted on mammals and yeast 17,18,19 , with much lesser attention to plants 20,21,22,23,24,25 . Analysis of genomic variation in rice was recently presented by McCouch et al. 26 (1,568 diverse inbred rice varieties analyzed at 700,000 SNPs), Huang et al. 27 (rice domestication analysis based on 1,083 cultivated indica and japonica varieties), Xu et al. 28 (resequencing of 50 accessions at >15 × raw coverage), Duitama et al. 29 (sequencing and bioinformatics analyses of 104 rice varieties belonging to the major subspecies of Oryza sativa), Arai-Kichise et al. 30 (analysis of SNP in seven rice cultivars of temperate and tropical japonica), Jain et al. 2 (whole-genome resequencing of three rice cultivars with contrasting responses to drought and salinity stress).

Understanding DNA variability in plants is hugely important, as crops are the core of world’s agriculture. Food consumption is predicted to double within the next 35 years, which translates in the demand for 2% annual yield growth rates for major crops. Yet, current breeding and selection methods, including transgenic technology, can only support on average a 1.1% annual growth rate 31 . Rice is the key cereal for the majority of the global population, especially in Asia and Africa. Climate change and increasing lack of agricultural land pose further challenges to rice production. Achieving the required increase of global production by up to 50% by 2050 32 will require new technologies, such as genomic selection and genome editing. Genomic selection depends on selection of those SNPs that favorably contribute to phenotype. Generally, success of new computational methods relies on understanding the specifics of plant biology, not shared with animals, such as self-fertilization, polyploidy and higher genetic variability 33,34,35 . Also, dicot and monocot plants have their own unique genetic features, and, therefore, one cannot liberally extrapolate knowledge from one plant to another, for example from the model dicot Arabidopsis thaliana to grasses 36,37,38 .

Here, we present a large-scale study on genetic variability in the rice genome, based on high quality data from the SNP-seek database, with over 40 million SNPs from 3,000 rice genomes 25 , the largest source of plant SNPs to date. Accessing this unique database allowed us to precisely describe genome-wide patterns of variability and to detect pronounced patterns not seen before. We provide new evidence on close association between local genetic variability and functionality, and suggest that the patterns of SNP density can help to detect functionally important genomic regions that are essential for crop improvement. Thus, we discovered specific characteristic patterns near sites with well-defined biological functions, such as transcription and translation start and stop sites, promoters, intron/exon junctions and 5′ and 3′ UTRs. Some features are similar to those in animals, while the others are novel and unique. In addition, we calculated relative SNP density on different groups of genes and demonstrated that transcription factor-encoding genes are highly conserved, whereas kinases and membrane-bound transporters are the most variable gene families.

11 - Plant diversity I – the greening of the land

In 1994 David Noble, a New South Wales National Parks and Wildlife officer, abseiled into a gorge in the Great Dividing Range west of Sydney. He found himself in a small stand of distinctive trees, with bark like bubbling chocolate, strappy, leathery leaves and crowns emerging above the rainforest. Experts examined the specimens and recognised the plant as belonging to the family Araucariaceae, which includes the hoop, kauri, Norfolk Island and the bunya pines. Excitingly, it was close to fossil specimens of pollen and leaves dating from the Cretaceous (Plate 11.1a). The species has thus existed for possibly 150 million years, once occurring over much of Gondwana. Its discovery was equivalent to finding ‘a small living dinosaur’.

The species was named Wollemia nobilis , a nice tribute to its discoverer, and is commonly known as the Wollemi pine, from the national park where it occurs. There are about 100 trees in two groves, and molecular techniques detected no genetic diversity between individuals although seeds are produced and germinate in nature.

The future of the remnant population is precarious because fire, pest or disease introduced into its small canyon could wipe out the species with its limited genetic diversity. The best measure to protect it is keeping the exact location secret. Unauthorised visitors may already have introduced the root-rot pathogen Phytophthora cinnamomi to the population.

Supporting information

S1 Fig.

Relative frequency of plant growth forms (herb, shrub, or tree) across the New World derived from disaggregated (left panel, BIEN) versus aggregated (right panel, GIFT) plant diversity data. (Left) Data from BIEN were obtained through the BIEN r-package by downloading species lists and trait information for 399 geographical units from the New World available in GIFT. The BIEN data set comprised 131,041 species, 969,625 species-by-region combinations, and 69,070 species-by-trait combinations. (Right) The GIFT data set was assembled according to the methodology described in case study 1 and comprised 117,163 species, 940,541 species-by-region combinations, and 89,515 species-by-trait combinations. BIEN, Botanical Information Network and Ecology Network GIFT, Global Inventory of Floras and Traits.

S2 Fig.

Relative frequency of plant growth forms (herb, shrub, or tree) across central Africa derived from disaggregated plant diversity data (left panel, RAINBIO) versus model predictions derived from aggregated plant diversity data (right panel, GIFT). (Left) High-resolution plot data from RAINBIO were aggregated to varying spatial resolutions following Watson and colleagues and matched with growth form data available in RAINBIO. (Right) Predictions of growth form composition are based on multinomial logistic regression of a global data set of species checklists and growth form information extracted from the GIFT database (see case study 1 for methodology). GIFT, Global Inventory of Floras and Traits.

S3 Fig.

Data set comparison for case study 2 between Moles and colleagues (2007) (11,481 species-by-sites combinations, upper plot) and GIFT (519,812 species-by-region combinations, lower plot). GIFT, Global Inventory of Floras and Traits.

S1 Data. Supporting Data for case study 1.

S2 Data. Supporting Data for case study 2.

Watch the video: Plant Diversity Part b: Adaptation enabling plants to move to land (January 2023).