Are a bonsai tree and its normal version the same species?

Are a bonsai tree and its normal version the same species?

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I would like to know if bonsai type of a tree is a separate species from its normal version. Do they differ by genes, or is it just a matter of different physical care?

Can I grow a bonsai tree from seed of a normal outdoor tree (of course, I understand that not every species will endure being dwarfed)? Can I grow a normal outdoor tree from some seeds labeled "bonsai"?

Yes. For any species that it is possible to grow as a bonsai tree, it is also possible to grow that species as a non-bonsai tree. The process of creating a bonsai tree involves growing a tree in a severely restricted pot with a minimal amount of soil, and trimming both the roots and branches to maintain a desired shape and size. It is the small growing space and regular pruning which keeps bonsai trees small, not the genetics of the tree.

You may wish to check the list of species used in bonsai on Wikipedia to get a sense of which species make good bonsai plants.

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The Arnold Arboretum, managed by Harvard University, is the oldest arboretum in the United States. It has a collection of 15 000 living woody plants and an associated herbarium of 80 000 specimens, many of which were taken from numbered plants still growing on the grounds. Herbarium specimens are dried, flattened plant specimens, mounted on sheets, with label information describing when and where they were collected. Often plants are collected in full flower for use in later studies of plant taxonomy and morphology. After examining these herbarium specimens and using our knowledge of species biology, living plants were selected for study based on the following criteria: (1) plants that produce conspicuous, easily recognizable flowers (2) plants that have an abrupt onset and fairly rapid decline of flowers, i.e., bloom for a relatively short time (3) plants that represent wild species (either native or introduced) rather than cultivars and hybrids, to minimize unknown alterations of plant physiology and of greatest importance, (4) only individuals for which there was at least one herbarium record of that plant in peak flower (at least half of the flowers were open) were selected for this study. Using these criteria, we selected 229 living plants for which there were 372 herbarium records of time of flowering between 1885 and 2002 (see Supplemental Data accompanying the online version of this article) some individual plants were represented by more than one herbarium specimen. These plants were contained in 37 genera. Genera that had at least 10 individuals in the sample are Amelanchier, Cornus, Corylopsis, Enkianthus, Halesia, Magnolia, Malus, Prunus, Rhododendron, and Syringa. All specimens are woody plants, including trees, shrubs, and vines. Individual plants are grown well spaced in conditions considered ideal for the species, which includes mulching, weeding, and pesticide and fertilizer applications when needed.

During the spring and summer of 2003, the same two people observed these individually numbered plants weekly between 13 April and 14 July. The observers determined the current peak flowering date and duration of flowering for each plant. Plants were recorded as being in one of four stages: not flowering, almost in full flower, full flower, or past full flower. A plant in full flower was defined as having at least 50% of its buds in full bloom and as being suitable for making a herbarium specimen. Once a plant was recorded as past flower, it was no longer observed.

A single Julian date of full flower was determined for each plant in 2003, although this date could have missed the true flowering peak by 3–4 d due to sampling just once a week. In cases when full flowering was observed on multiple dates, the mean of the Julian dates for those days was used. Once the date of full flowering was determined for each plant in 2003, these dates were compared with flowering dates based on the herbarium records. For each record, the Julian date of peak flowering in 2003 was subtracted from the Julian date of the past flowering date to estimate a change in plant flowering dates. In effect, the flowering dates of 2003 were used as a standard against which flowering times in other years were compared. The spring (February through May) of 2003 was colder than any previous year since 1967 and was more typical of temperatures early in the 20th century. Using these changes in flowering dates for individual plants, we used multiple regression analysis to examine how flowering times across all species have changed over time and how this change compares to the trend of warming spring temperatures in Boston. We estimated the following equation: ΔFT = ɑ́ + B1ΔTemp + B2ΔTime + μ, where ΔFT, ΔTemp, and ΔTime are the difference between the flowering time, temperature, and years, respectively, in 2003 and a past year in which a herbarium specimen was collected. ɑ́ is a constant, B1 and B2 are regression coefficients, and μ is a normally distributed random error term.

Over the last 100 yr, Boston has experienced an annual temperature increase of 1.5°C (Fig. 1), which has been due to regional climate change and the urban heat island effect (New England Regional Assessment, 2001). We hypothesized that, given this warming trend, analysis of herbarium samples would demonstrate that plants are responding to a warmer climate by flowering earlier. We believed that the main drawback of using herbarium samples to determine peak flowering date would be the deviation between the dates of collection and peak flowering that is, people in the past might have collected specimens early or late in the flowering season, obscuring trends in flowering times. We investigated this area further in our analysis.


Many non-native species profoundly alter communities they invade through competition, hybridisation, disease transmission and other mechanisms (Kumschick, Alba, Hufbauer, & Nentwig, 2011 ). Such impacts threaten the presence of native taxa, and have contributed to species extinctions (Bellard, Cassey, & Blackburn, 2016 ). The extent and magnitude of impacts of invasions are increasing globally, and methods for identifying and quantifying them more efficiently are urgently needed. The link between impact and biogeographical origin is, however, contentious. Non-native species are sometimes the drivers and at other times the result of global change (MacDougall & Turkington, 2005 ), and many plant species are agricultural and/or environmental weeds, even within their native ranges (Randall, 2017 ).

Some authors have suggested that further comparisons are needed for species that are weedy both in their native and non-native ranges to make progress in the field of invasion science (Hufbauer & Torchin, 2008 ). For example, identifying weedy native plants can be useful for management and species that are prone to becoming weedy (i.e., expanding rapidly, encroaching or having transformative impacts) following disturbance are more likely to become problematic when introduced to similar habitats (Caley & Kuhnert, 2006 Davis et al., 2010 ). Moreover, controlling weedy natives and non-natives concurrently is often necessary to promote the rehabilitation of ecosystems. When weedy natives become dominant they often reduce populations of other native species (Yelenik, Stock, & Richardson, 2004 ). And, when management focuses on non-natives only, for example, through clearing, resultant disturbances often cause native communities to become dominated by other weedy or ruderal species.

Though native species can display weedy habits under specific conditions, there is general consensus that invasive non-native species have greater environmental impacts (Hassan & Ricciardi, 2014 Meiners, Steward, & Cadenasso, 2001 Paolucci, Macisaac, & Ricciardi, 2013 Simberloff, Souza, Nuñez, Barrios-Garcia, & Bunn, 2012 Taylor, Maxwell, Pauchard, Nuñez, & Rew, 2016 ). A 40-year study reviewing abandoned agricultural land found that invasions by non-native species had a stronger effect than native weeds on overall species richness (Meiners et al., 2001 ). This pattern is generally consistent for plants (Simberloff et al., 2012 Taylor et al., 2016 ) and animals (Hassan & Ricciardi, 2014 Paolucci et al., 2013 ). These findings suggest that origin status (i.e., native or non-native) influences the magnitude and type (i.e., mechanism) of environmental impacts that occur when a species becomes weedy and forms a dominant component of communities.

Bamboos (Poaceae: Bambusideae) are an excellent group for exploring the relevance of biogeographic origin when considering impacts caused by weedy species. A growing number of studies have addressed the impacts of bamboos in both their native and non-native ranges for several reasons: (1) bamboos have an extensive distribution both naturally and because they have been widely redistributed around the world by humans (Canavan et al., 2017 ) (2) bamboos are often dominant components of vegetation—a change in abundance can therefore have strong effects on community structure and functioning (3) species that are known to have impacts are not always the same as those with capacity for rapid dispersal, that is, to become invasive (Canavan et al., 2017 Richardson, Pyšek, & Carlton, 2011 ) and (4) bamboos are perennial forest grasses and therefore have a unique interaction with trees compared to other grass groups (Soderstrom & Calderon, 1979 ). Forest systems are generally less studied in invasion science than other major habitat types, such as grasslands (Levine, Adler, & Yelenik, 2004 ), and they are considered to be generally inherently less susceptible to invasions by non-native species than most other habitats (Crawley, 1987 Von Holle, Delcourt, & Simberloff, 2003 ). Therefore, studying bamboos might provide insights into a facet of invasion science that has not received much attention (Martin, Canham, & Marks, 2009 ).

We reviewed the literature on the environmental impacts caused by invasion (i.e., the spread of non-native species) and expansion (i.e., the spread of weedy native species) of bamboos. We then used the International Union for Conservation of Nature's (IUCN) Environmental Impact Classification of Alien Taxa (EICAT) scheme (Blackburn et al., 2014 Hawkins et al., 2015 ) to score the impact type and magnitude in the native and non-native ranges. We expected to find greater impacts in the non-native range where bamboos might have fewer pressures controlling their populations, and that the types of impacts would be different for native and non-native species. We also tested whether the habitats where impacts are described are similar in native and non-native ranges.

- Biology , 6th Edition, Raven et al, 2002: 455.

“A clear line of fossils ”? Fradulent statements like this, ubiquitous in evolution-based college textbooks (e.g., Figs. 1-7 ), will be the downfall of science if the community does not distance itself from the blatant us e of fraud to manipulate people’s beliefs. Anthropology, biology, and paleontology have become a conglomerate easily provable to employ fraud in the captive-audience science classroom. Except that they’re being paid, I would not want to be the AAAS or an attorney representing mainstream science at this point.

When I was a boy in 1960s Michigan there were several things I wanted to be when I grew up. They included, paleontologist (see Tales of a Fossil Collector in this issue) marine biologist astronaut artist/musician and detective or attorney.

As far as the desire to be an attorney goes, it was inspired by the television program, Perry Mason —excellent television giving a sense of cr itical thinking until the show ended in 1966. But right on the heels of Perry Mason (and no less, the thought-provoking series, The Outer Limits ), just a few months later began the baby-boomer life-changing phenomenon of Star Trek .

One typically hears how Star Trek influenced modern technology. That’s obviou s. However, I would like to say that one of Star Trek’s biggest influences on me as a 12-year old was Science Officer Spock’s constant referral to logical thinking. Of course, I also admired Captain Kirk et al.

This whole notion of logical or critical thinking led me to the school library and a book on logic . That is when (unrelated to any classes) I first learned about logical fallacies, over-generalization, circular reasoning, black & white thinking, etc., all of which are generally considered bad science. It was many years later I discovered that these are traits of evolutionary fanaticism. The logic book also brought me to Plato and eventually reading many of his dialogues, learning perspective, Theory of Forms, and a general sense of putting actual effort into thinking.

Fig. 1 . Biology, 10th Ed., Raven et al , 2013. Like all similar textbooks this series is packed with fraudulent statements presented as fact.

So, that is where my idealized expectations of science came from. However, as most readers already know, after experiencing censorship of empirical evidence starting with a paper called The Impact of Fossils on the Development of Visual Representation (again, see Tales of a Fossil Collector ), and later, The Graphics of Bilzingsleben , awareness of publication control by evolution fanatics began to emerge and trust in peer review as ‘science’ appropriately dissolved to nothing. Regarding the censorship of Fossils, archaeologist Paul Bahn wrote me that Current Anthropology published “a lot of rubbish” while blocking good papers. Anthropologist Randy White expressed identical sentiment regarding the censorship as did many other leading authorities. Censorship makes deception possible by removing the means to assess evidence objectively. False statements then become unrecognizable even to textbook writers and very few will even bother investigating evidence for themselves. This is how textbooks enable fanatics to control the public mind. They are going to need dozens of attorneys defending them once the scope of this deception cracks open.

Fig. 2. The Earth Through Time , 7th Ed. (2003) is “historical” geology, i.e. not objective geology but that absorbed by evolutionism. Every edition is packed with false statements or speculations rendered as fact. Like Historical Geology , this book is beautifully produced. It is only its evolutionism that makes it a work of propaganda.

18.) “Most fossil intermediates in vertebrate evolution have indeed been found.”

- Biology , 6th Ed. Raven et al. 2002: 455.

This is an outright fraudulent statement that is not even close to being true as the following quotes will attest. The same is the case for invertebrates with literally zillions upon zillions upon zillions of fossils (you have to

"Paleoanthropologists make educated guesses about which fossil species represent ancestors that live at the branch points of the cladogram. "

- Evolutionary Analysis , Freeman and Herron, 1998: 541-2.

get out into the field to know this) none of which show any “clear line.” In other words, the statement proves that the authors of a leading biology textbook either have no idea what they’re talking about when it comes to the fossil record or are participants in fraud. Still, it is presented to trusting students as fact. One way deceptions like this thrive is that each field in the template-thinking conglomerate—biology-paleontology-anthropology—keeps duping the other while individuals in each group have no grasp of the issues from outside the conglomerate. Put the experts on the stand and they won’t repeat this statement without qualification, as only an easily-duped judge such as Judge Jones could buy it (I have read the Kitzmiller v. Dover transcript—it is packed with trickery). No one who knows fossils, strata , or capabilities of time would support the statement on the stand. If they did it would enable a single on-the-ball opposing attorney to crack wide open the entire mindset in one fell swoop.

Fig. 3. The Earth Through Time , 10th Ed. (2013). Being “historical geology” (i.e. Darwinism rather than objective geology), every edition, like all textbooks in the genre, is filled to the brim with fictions taught as fact.

19.) “The fossil record provides a clear record of the major evolutionary transitions that have occurred through time.”

- Biology , 6th Ed. Raven et al. 2002: 441

Fig. 4. Evolutionary Analysis , upcoming 5th Edition, Freeman et al, 2013. Don’t expect any surprises. Prediction: the reader should find as much fiction fanatically stated as fact as in prior editions using rhetorical intimidation a.k.a. Richard Dawkins style.

20.) “A clear line of fossils now traces the transition between whales and hoofed mammals… reptiles and mammals… dinosaurs and birds… apes and humans.”

– Biology , 6th Edition, Raven et al, 2002: 455.

Despite the boldness with which the Biology textbook makes the above false statement it regularly contradicts itself as do all such textbooks. To assess the value of the statement consider the following concessions from another textbook. It should be obvious that there is general knowledge in biology, paleontology, and anthropology that they are making false claims. Admission that what they are saying is not true is at the heart of textbook deception:

21.) “Although some may find it frustrating, human evolution is just like that of other groups in that we have followed an uncertain evolutionary path.”

- Historical Geology , 5th Ed, Wicander et al., 2007: 398.

Frustrating is clearly not the right word. Historical Geology presents evolution as a fact yet in moments of lucidity, like this one, they come right out and admit that there is nothing clear about the claims at all. They emphasize this point a few pages further in:

22.) “There is no clear consensus on the evolutionary history of the hominid lineage.”

- Historical Geology , 5th Ed, Wicander et al., 2007: 402.

Fig. 5. Life: The Science of Biology (Vol. II). Every edition loaded with false statements of fact.

23.) “Humans arose from australopithecine ancestors. Many experts believe that the recently discovered Australopithecus garhi or a similar species gave rise to the genus Homo .”

- Life: The Science of Biology , 6th Ed. (Vol. II: Evolution, Diversity, and Ecology Purves et al., 2001): 597.

Evolutionary doublespeak. Here the fiction is first presented as fact followed by a direct admission it is “belief.” Students find no discrepancy between a statement of fact and the same statement reiterated as a belief.

24.) “One can draw the hominid family tree in two very different ways, either lumping variants together or splitting them into separate species.”

- Biology , 6th Ed. Raven et al. 2002: 477.

A few pages earlier the authors state as fact that there is a “clear line of fossils” between apes and humans (p. 455). If there is a clear line of fossils then why all the interpretation? Here the authors admit that they don’t even know if various hominid fossils are different species. This isn’t exactly unimportant when it comes to the idea of evolution. The quandary applies to all fossils.

25.) “The fossil database for hominids is frustratingly sparse.”

26.) “Paleoanthropologists …make educated guesses about which fossil species represent ancestors that live at the branch points of the cladogram…”

- Evolutionary Analysis , Freeman and Herron, 1998: 538, 541-2 .

- Historyical Geology , 5th Ed, Wicander et al., 2007: 398

27.) “Early in its evolutionary history, the primate lineage split into two main branches. …Too few fossil primates have been discovered to reveal with certainty their evolutionary relationships.”

- Life: The Science of Biology , 6th Ed. (Vol. II: Evolution, Diversity, and Ecology Purves et al., 2001): 595.

As above, this is typical evolutionary doublespeak the first sentence is stated as fact while the following sentence (in the referred figure) shows it was a false statement.

28.) “Any single evolutionary scheme of hominid evolution presented here would be premature.”

- Historical Geology , 5th Ed, Wicander et al., 2007: 404.

So the authors say, and in this form, it almost sounds scientific. However, a few pages further the textbook proceeds to tell students exactly how humans evolved as if it had never said otherwise:

29.) “The oldest known hominid is Sahelanthropus . . It was followed by Orrorin . then. Ardipithecus . … Recent discoveries indicate Ardipithecus evolved into Australopithecus . . The human lineage began. with the evolution of Homo habilis . . Homo erectus evolved from Homo habilis . . Homo sapiens evolved from H. erectus .”

- Historical Geology , 5th Ed, Wicander et al., 2007: 410.

The human evolution mythology presented as a fact. The authors even misuse a trusted scientific word, “indicate.” “Indicate” expresses a certainty. There is no more certainty that Ardipithecus evolved into Australopithecus than that bonobos evolved into Australopithecus .

30.) “The footprints [the 3.6 million-year old Laetoli, Tanzania, human footprints] confirm skeletal evidence that the species [ Australopithecus afarensis ] had a fully erect posture.”

- The Earth Through Time , 7th Ed., HL Levin, 2003: 552.

31.) “These fossil footprints. are not human. … They record… Australopithecus , the group from which our genus, Homo , evolved. …Human evolution is the part of the evolution story … which we know the most.”

- Biology , 6th Ed. Raven et al. 2002: 477.

This ongoing myth of australopithecine posture being confirmed by the Laetoli footprints is false. There is no association between the two. The myth was started by Donald Johanson (discoverer of Lucy) who commandeered the footprints from their discoverer, Mary Leakey. Leakey was about to introduce them as the oldest “human” footprints (D. Ellis, The Leakey Family: Leaders in the Search for Human Origins , 1978: 100). Leakey should not have accepted Johanson’s takeover of the Laetoli footprints. Instead, she simply responded with her deep regret that “the Laetoli fellow is now doomed to be called Australopithecus afarensis .”

32.) “Make no mistake about it. They are like modern human footprints.”

–Tim White, excavator of the Laetoli footprints Lucy: The Beginnings of Humankind , by Donald Johanson.

33.) “Because of the recent controversy concerning the teaching of evolution in the public schools. how would you go about convincing the school board that humans have indeed evolved from earlier hominids?”

- Historical Geology , 5th Ed, Wicander et al., 2007: 404.

This is clearly not a normal science question. Modern academia tries to convince students of evolution any way it can. In this particular instance the captive audience science classroom is used to ask a “leading question” of students on an obviously debatable subject. It shows the type of thinking skills students are given as they go through academic training and are sent out into the world. It is not a question for critical thinking. It is one for simple memorization as noted in the Prologue quotes of Part 1. It also shows part of how higher institutional education produces graduates without scientific objectivity but with an agenda attached (See Part 2).

If human evolution is the part of the evolution story the authors of Biology claim we “know the most” then the few quotes provided in this installment should show that the whole paradigm is in trouble. It is no wonder that students who graduate with degrees in the evolution conglomerate come out reliant on techniques of propaganda (Part 1) as a defense for their training. As shown, neither students nor textbook writers are able to distinguish facts from fiction when it comes to evolution. Students are trained not to look into the evidence—or lack—for themselves. For them, the only option is to believe that somewhere out there paleontologists have all this overwhelming fossil evidence they keep hearing about. So, in the final turn, what we are actually talking about is faith. Faith is a part of all science and is fine except when promoting a myth of origins as fact while withholding relevant evidence that does not support the myth. That circumstance is not science.

John Feliks has specialized in the study of early human cognition for nearly twenty years demonstrating beyond any reasonable doubt that human cognition does not evolve. His work and empirical geometric evidence have been censored by the evolution community. Earlier, his focus was on the fossil record studying fossils in the field across the U.S. and parts of Canada as well as studying many of the classic texts Treatise on Invertebrate Paleontology , Index Fossils of North America , etc.). He wrote the article, Ardi: How to Create a Science Myth, and claims that all pre-human hominids or similar claims for transitional invertebrate fossils are equally as easy to debunk because when the paradigm is flawed it is not difficult to debunk everything it contains. Feliks encourages students going through standard science training to openly question the ideology being forced upon them as fact in the captive audience science classroom with full confidence that evidence is there to support them.

Feliks, J. 2012. Five constants from an Acheulian compound line . Aplimat - Journal of Applied Mathematics 5 (1): 69-74.

Are a bonsai tree and its normal version the same species? - Biology

Compiler: IUCN/SSC Invasive Species Specialist Group (ISSG)

Review: Dr. Colin Hughes, Department of Plant Sciences, University of Oxford, OXFORD, UK.

Publication date: 2010-08-16

Recommended citation: Global Invasive Species Database (2021) Species profile: Leucaena leucocephala. Downloaded from on 24-06-2021.

A Risk Assessment of Leucaena leucocephala for Hawai‘i and other Pacific islands was prepared by Dr. Curtis Daehler (UH Botany) with funding from the Kaulunani Urban Forestry Program and US Forest Service. The alien plant screening system is derived from Pheloung et al . (1999) with minor modifications for use in Pacific islands (Daehler et al. 2004). The result is a score of 15 and a recommendation of: "Likely to cause significant ecological or economic harm in Hawai‘i and on other Pacific Islands as determined by a high WRA score, which is based on published sources describing species biology and behaviour in Hawai‘i and/or other parts of the world."

A Risk assessment of Leucaena leucocephala for Australia was prepared by Pacific Island Ecosystems at Risk (PIER) using the Australian risk assessment system (Pheloung, 1995). The result is a score of 11 and a recommendation of: reject the plant for import (Australia) or species likely to be of high risk (Pacific).

Biological: A bruchid beetle seed predator, Acanthoscelides macrophthalmus has been deliberately introduced and released in South Africa as a biocontrol agent and the same insect has been accidentally introduced to Australia. The accidental spread of the psyllid insect defoliator Heteropsylla cubana in the mid 1980s can cause cyclical defoliation, but does not kill trees and the psyllid appears to have been brought under control by a number of generalist local (and in some cases introduced) psyllid predators and parasites.

Integrated management: Once established, Leucaena is difficult to eradicate. It resprouts vigorously after cutting. Cut stumps need to be treated with diesel or other chemicals. Furthermore, the soil seed bank can remain viable for at least 10-20 years after seed dispersal.


Through employing a comparative approach which incorporates data from multiple populations of all 19 currently recognised gibbon species, we revealed both intrinsic and extrinsic drivers of home range size, social group size and mating system across the Hylobatidae. Home range, group size and mating system are all strongly phylogenetically conserved in gibbons, meaning that more closely related gibbon species resemble each other in terms of these behavioural and ecological traits more than expected by chance. Once these phylogenetic signals are accounted for, variation in these key traits is driven by a combination of social and external factors: variation in gibbon home range size is explained by gibbon group density at a site along with mating system (monogamy versus polygyny) and social group size gibbon social group size is linked to mean annual rainfall (at the site level) and mating system and, while no explanatory variables were statistically associated with mating system, gibbon mating system, group size and home range appear to be inherently linked traits, with these factors being important, inter-correlated predictors of each other.

By formally contextualising the Hainan gibbon’s observed behavioural and ecological characteristics within family-wide variation in gibbons, we were also able to determine natural population parameters expected for this Critically Endangered species, compared to those that may be driven by current site conditions experienced by the sole remaining Hainan gibbon population. Our results indicate that remnant Hainan gibbon social groups at Bawangling have larger home ranges than expected in the context of the strong phylogenetic signal across the Hylobatidae, which may be a result of the critically low population density and thus group density at this site. However, current Hainan gibbon group size is no larger than predicted from the pattern of phylogenetic relationships alone, and there is no evidence that the observed mating system (polygyny) is driven by any currently existing external drivers, indicating that large, polygynous groups may be the normal social structure for the Hainan gibbon. Our findings therefore have important and direct implications for Hainan gibbon conservation planning, but also more widely enhance our understanding of gibbon ecology. Our study also demonstrates the usefulness of the comparative approach for informing management of species of conservation concern.


A common assumption made by several studies incorporating evolutionary information into community ecology research is that the nature and strength of species interactions depends on the phylogenetic relatedness of species, with competition being strong among close relatives and facilitation occurring primarily among distantly related taxa (Fig. 1). Results of our mesocosm experiments using a pool of eight species of freshwater green algae showed that neither competitive nor facilitative interactions depended on the evolutionary relatedness of interacting species for this group of organisms.

Although we found evidence of facilitation in roughly one-quarter of the pairwise species interactions, the prevalence of this form of interaction did not depend on the evolutionary relatedness of species. This result contradicts evidence from recent field studies suggesting that the prevalence of facilitative interactions tends to be most common between distantly related species (Valiente-Banuet & Verdú 2007 , 2008 Castillo, Verdú & Valiente-Banuet 2010 see Verdu, Gomez-Aparicio & Valiente-Banuet 2012 for a review). By classifying over 450 angiosperm species as ‘non-facilitated’ (i.e. species recruiting on open ground) or ‘facilitated’ (i.e. species recruiting under vegetation), Valiente-Banuet & Verdú ( 2007 ) determined that a trait they called the ‘regeneration niche’ was strongly conserved. As a consequence of phylogenetic conservatism of the regeneration niche, nurse plants more frequently facilitate distantly related species than closely related species (Valiente-Banuet & Verdú 2007 ). Under the assumption that traits related to facilitation are evolutionarily conserved, it is expected that the prevalence of facilitative interactions will increase with phylogenetic distance. In accordance with this trend, Castillo, Verdú and Valiente-Banuet ( 2010 ) found that the performance of a cactus (Neobuxbaumia mezcalensis) was positively influenced by the presence of neighbour ‘nurse’ plants and this positive effect increased as the neighbours were less related to the cactus. Similarly, Verdu, Gomez-Aparicio and Valiente-Banuet ( 2012 ) found that the less related the neighbouring plants were to a focal plant, the greater the positive effect on the nurse plant growth. However, in an observational study carried out in Spanish steppes (Soliveres, Torices & Maestre 2012 ), the effect of relatedness on the prevalence of facilitative interactions among species proved to be more mixed. The growth of the grass Stipa tenacissima was not affected by relatedness of its neighbours. The growth of a shrub Quercus coccifera was negatively influenced by the presence of nurse species within a range of relatedness of 207–273 million years and facilitated by the presence of closer or more distantly related species. As may be true for traits related to competition, it is possible that the traits involved in facilitative interactions are not phylogenetically conserved in the green algae included in our experiment.

Recently, some progress has been made depicting the functional traits controlling competition and community structure in freshwater phytoplanktonic systems (Litchman et al. 2010 Edwards, Klausmeier & Litchman 2011 Schwaderer et al. 2011 ). For instance, when considering a very large range of algal taxonomic groups, there is evidence that competitive abilities for nitrate and phosphate are negatively correlated, suggesting that species performing well under nitrate limited conditions perform badly under phosphate limited conditions and vice versa (Edwards, Klausmeier & Litchman 2011 ). Moreover, traits linked to light utilization and maximal growth rates have been successfully used to predict phytoplanktonic community structure across U.S. lakes (Edwards, Litchman & Klausmeier 2013 ). For these large taxonomic groupings, cell size may also act as a master trait influencing phytoplankton community structure, with large-celled taxa having an advantage under the nutrient-abundant or nutrient-fluctuating conditions and small taxa being favoured under nutrient-restricted conditions (Litchman et al. 2010 Edwards, Klausmeier & Litchman 2011 ). Unfortunately, the phylogenetic signal of traits related to competition and facilitation has not yet been conducted.

Overall, the evolution of facilitative traits remains minimally explored (Bronstein 2009 ) and the functional traits responsible for facilitative interactions in plants are being investigated (Butterfield & Callaway 2013 ). While there has been some work done already to identify the traits responsible for the outcome of competition in freshwater algae (Tilman 1981 Edwards, Litchman & Klausmeier 2013 ), there has been virtually no work investigating facilitative or mutualistic interactions in these species. Without direct evidence, our hypotheses regarding these traits are currently speculative. Facilitative interactions may involve the ability of one species to provide resources such as vitamins or other organic molecules (produced as metabolites) to another in the form of cross-feeding relationships. Some species may also modify water chemistry (e.g. pH or dissolved CO2 concentrations) or light availability in a beneficial way for their competitors. Testing the mechanisms by which Scenedesmus acuminatus and Chlorella sorokiniana benefited from the presence of other algae in this study will be an avenue for future work.

The lack of universality of niche conservatism, the absence of phylogenetic signal in traits relevant for competition and/or facilitation, and the limited support for the competition-relatedness hypothesis illustrate some of the difficulties and limitations of integrating phylogenetic information into community ecology research. The incorporation of a phylogenetic perspective into community ecology and ecosystem functioning research was initially based at least partially on the relative ease of measuring phylogenetic distance compared to measures of functional differentiation. Phylogenetic distance was hypothesized to represent a cheap and reliable metric capable of summarizing all of the ecological differences among species. Based on the results observed here and along with previous studies, we have no reason to believe phylogenetic distance alone should generally predict the outcome of competition or facilitative interactions. Our results suggest that the phylogenetic relatedness of species may not be a reliable proxy for functional or ecological similarity and may not be used to infer the forces determining the structure of ecological systems. Although, additional studies relating the nature and strength of species interactions to the evolutionary relatedness of species of other taxa and the investigation of this relationship at other phylogenetic scales are required to validate the generality of our findings.


While it is possible that a sturgeon and paddlefish could mate, it&rsquos incredibly unlikely they would due to their geographic locations. If they did, their hybrid offspring would be called the Sturddlefish, and it&rsquos one incredibly unique creature. Both the American Paddlefish & Russian Sturgeon are endangered. This led to programs being done to breed both in captivity. The hope was to preserve both species&hellipbut they never realized what would happen.

[Image via The New York Times] Researchers in Hungary were part of those conducting experiments. Not all sea creatures can breed in captivity, so this was a crapshoot for sure. During this ordeal, they used sperm from a paddlefish with a female sturgeon. The idea was that it would act as a &ldquocontrol&rdquo when fertilizing the eggs later with sturgeon sperm. Researchers then essentially created the Sturddlefish on accident, and have continued making them.

Results and discussion

Mistnet's view of North American bird assemblages

I began by decomposing the variance in mistnet's species-level predictions into variance among routes (which varied in their climate values) and residual (within-route) variance (Appendix S6). On average, the residuals accounted for 30% of the variance in mistnet's predictions, suggesting that non-climate factors play a substantial role in habitat filtering.

If the non-climate factors mistnet identified were biologically meaningful, then there should be a strong correspondence between the 15 coefficients assigned to each species by mistnet and the AAB habitat classifications. A linear discriminant analysis (LDA Venables & Ripley 2002 ) demonstrated such a correspondence (Fig. 4). Mistnet's coefficients cleanly distinguished several groups of species by habitat association (e.g. ‘Grassland’ species vs. ‘Forest’ species), though the model largely failed to distinguish ‘Marsh’ species from ‘Lake/Pond’ species and ‘Scrub’ species from ‘Open Woodland’ species. These results indicate that the model has identified the broad differences among communities, but that it lacks some fine-scale resolution for distinguishing among types of wetlands and among types of partially wooded areas. Alternatively, perhaps these finer distinctions are not as salient at the scale of a 40-km transect or require more than two dimensions to represent.

While one might be able to produce a similar-looking scatterplot using ordination methods such as non-metric multidimensional scaling (NMDS McCune, Grace & Urban 2002 ), the interpretation would be very different. species' positions in ordination plots are chosen to preserve the multivariate geometry of the data and do not usually connect to any data-generating process or to a predictive model. In Fig. 4, by contrast, each species' x–y coordinates describe the predicted slopes of its responses to two axes of environmental variation these slopes could be used to make specific predictions about occurrence probabilities at new sites. Likewise, deviations from these predictions could be used to falsify the underlying model, without the need for expensive permutation tests or comparison with a null model. The close connection between model and visualization demonstrated in Fig. 4 may prove especially useful in contexts where prediction and understanding are both important.

The environmental gradients identified in Fig. 4 are explored further in Fig. 5. Figure 5a shows how the forest/grassland gradient identified by mistnet affects the model's predictions for a pair of species with opposite responses to forest cover. The model cannot tell which of these two species will be observed (since it was only provided with climate data), but the model has learned enough about these two species to tell that the probability of observing both along the same 40-km transect is much lower than would be expected if the species were uncorrelated.

Figure 5a reflects a great deal of uncertainty, which is appropriate considering that the model has no information about a crucial environmental variable (forest cover). Often, however, additional information is available that could help resolve this uncertainty, and the mistnet package includes a built-in way to do so, as indicated in Fig. 5b,c. These panels show how the model is able to use a chance observation of a forest-associated Nashville Warbler (Oreothlypis ruficapilla) to indicate that a whole suite of other forest-dwelling species are likely to occur nearby and that a variety of species that prefer open fields and wetlands should be absent. Similarly, Fig. 5d shows how the presence of a Redhead duck (Aythya americana) can inform the model that a route likely contains suitable wetland habitat for waterfowl, marsh-breeding blackbirds, shorebirds and rails (along with the European Starling and Bobolink, whose true wetland associations are somewhat weaker). None of these inferences would be possible from a stack of disconnected single-species SDMs, nor would traditional ordination methods have been able to quantify the changes in occurrence probabilities.

Model comparison: species richness

Environmental heterogeneity plays an especially important role in determining species richness, which is often overdispersed relative to models' expectations (O'Hara 2005 ). Figure 6 shows that mistnet's predictions respect the heterogeneity one might find in nature: areas with a given climate could plausibly be either very unsuitable for most waterfowl (Anatid richness <2 species) or much more suitable (Anatid richness >10 species). Under the independence assumption used for stacking SDMs, however, both of these scenarios would be ruled out (Fig. 6a).

Stacking leads to even larger errors when predicting richness for larger groups, such as the complete set of birds studied here. Models that stacked independent predictions consistently underestimated the range of biologically possible outcomes (Fig. 6b), frequently putting million-to-one or even billion-to-one odds against species richness values that were actually observed. These models' 95% confidence intervals were so narrow that half of the observed species richness values fell outside the predicted range. The overconfidence associated with stacked models could have serious consequences in both management and research contexts if we fail to prepare for species richness values outside such unreasonably narrow bounds (e.g. expecting a reserve to protect 40–50 species even though it only supports 15). Mistnet, on the other hand, was able to explore the range of possible non-climate environments to avoid these missteps: 90% of the test routes fell within mistnet's 95% confidence intervals, and the log-likelihood ratio decisively favoured it over stacked alternatives.

Model comparison: single species

Figure 7a compares the models' ability to make predictions for a single species across all the test routes (shown as the exponentiated expected log-likelihood). While there was substantial variation among species, the two neural network models' predictions averaged more than an order of magnitude better than BRT's. Moreover, these models' advantage over BRT was largest for low-prevalence species (linear regression of log-likelihood ratio vs. log-prevalence P = 3*10 −4 ), which will often be of the greatest concern to conservationists. The most likely reason for this improvement was a reduction in overfitting: while the overall model included complex nonlinear transformations, the number of degrees of freedom associated with any given species in the final logistic regression layer was modest (15 weights plus an intercept term).

BayesComm's predictions were substantially worse than any of the machine learning methods tested, which I attribute mostly to its inability to learn nonlinear responses to the environment (Elith et al. 2006 ). Adding quadratic terms or interaction terms (cf. Austin 1985 Jamil & ter Braak 2013 ) would have led to severe overfitting for many rare species. Even if one added a regularizer to the software to mitigate this problem, these extra pre-specified terms may still not provide enough flexibility to compete with modern nonlinear techniques.

Applying BayesComm to a large data set also highlighted one other area where mistnet appears to outperform existing JSDMs. Despite its assumed linearity, the BayesComm model required 70 000 parameters, most of which served to identify a distinct correlation coefficient between a single pair of species. Tracing this many parameters through hundreds of Markov chain iterations routinely caused BayesComm to run out of memory and crash, even after the code was modified to reduce its memory footprint. Sampling long Markov chains over a dense, full-rank covariance matrix (as has apparently been done in all other JSDMs to date) thus appears to be a costly strategy with large assemblages.

Model comparison: community composition

While making predictions about individual species is fairly straightforward with this data set (since most species have relatively narrow breeding ranges), community ecology is more concerned with co-occurrence and related patterns involving community composition (Chase 2003 ). Mistnet was able to use the correlation structure of the data to reduce the number of independent bits of information needed to make an accurate prediction. As a result, mistnet's route-level likelihood averaged 430 times higher than the baseline neural network's and 45 000 times higher than BRT's (Fig. 7b). BayesComm demonstrated a similar effect, but not strongly enough to overcome the low quality of its species-level predictions.

Author information


Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou, 01, PO BOX 526, Benin

Laurent G Houessou & Brice Sinsin

Department of Geography, Faculty of Letter, Arts and Human Sciences University of Abomey-Calavi, Cotonou, Benin

Toussaint O Lougbegnon, François GH Gbesso & Lisette ES Anagonou

National High School of Technical and Agronomical Sciences, University of Abomey-Calavi, Abomey-Calavi, PO Box 1967, Benin