Clarifying the mechanisms by which community composition changes in response to environmental variability is important for understanding biodiversity, productivity and ecosystem stability Hector and Bagchi, ; Ives and Carpenter, Traditional measures of community composition typically focus on species-level organization because species has long been considered the fundamental unit of biological classification Magurran, For example, preparing a site-by-species matrix observed species across sampling sites is usually a first step when examining the dynamics of community composition across time and space Magurran, Further, a common monitoring and assessment practice attempts to characterize community—environment relationships by combining the variation of species composition with environmental measurements Margules and Pressey, ; Hansen et al.
Note, however, that species-level community data are not always available because of constraints on time and the expertise required to properly identify all individuals to species level Warwick, ; Bailey et al.
Accordingly, the concept of taxonomic surrogacy or sufficiency that is, using broadly resolved taxonomic data as a substitute for species-level data has been subjected to extensive examination in bioassessment studies Warwick, ; Bailey et al.
Despite some controversial opinions about general applications for particular organisms and ecosystems, the use of broadly resolved data that is, genus-, family- or even phylum-level classification has been demonstrated to portray similar community-scale responses to environmental variability as species-level data in many empirical studies Olsgard et al.
Broad-level taxonomic data have been widely applied to various types of organisms in terrestrial, freshwater and marine environments as a means of optimizing the cost-effectiveness of detecting environmental impact on community organization Terlizzi et al. However, little attention has been devoted toward a mechanistic understanding of why broadly and finely resolved data can be similarly efficacious for the assessment of community—environment relationships but see Bevilacqua et al.
From the evolutionary perspective, the high efficacy of broadly resolved data may be explained by the conservation of habitat preferences among phylogenetically related taxa Webb et al. Review of some earlier studies Olsgard et al. Thus, there are a variety of potential mechanisms by which taxonomic resolution may influence the strength of community—environment relationships. Here we aim to bridge the concepts of taxonomic surrogacy and phylogenetic conservatism with an emphasis on the impact of niche conservatism or niche divergence on observed macro-evolutionary patterns Figure 1 ; Supplementary Figure S1.
A conceptual scheme for revealing the evolutionary effect regarding niche conservatism or niche divergence on community assembly. The strength of the community—environment relationship can be quantified by explained variance R 2 in multiple linear regression or redundancy analysis. See Supplementary Figure S1 for a conceptual example. We propose that evaluating the strength of community—environment relationships hierarchically along taxonomic ranks might serve as a means to infer the importance of evolutionary forces underlying community assembly.
It is theoretically possible that broadening taxonomic resolution does not weaken the strength of community—environment relationships if broader taxonomic units carry niche-related signals as strong as those associated with finer units Figure 1a ; Supplementary Figure S1a. This scenario might be expected when taxa belonging to the same lineage tend to exhibit similar responses to environmental factors Warwick, ; Bailey et al.
Moreover, when finely resolved taxa are hypothesized to be ecologically equivalent that is, they have equivalent fitness and potentially occupy the same niche space Leibold and McPeek, , broad-level taxonomic classification might be expected to enhance the strength of community—environment relationships Figure 1b ; Supplementary Figure S1b , as broader taxonomic grouping can balance the randomness caused by neutral-stochastic processes in spatiotemporal distributions associated with finer taxa, by summarizing occurrences and abundances of those ecologically equivalent units as a signal responder Warwick, ; Bailey et al.
In contrast, the strength of community—environment relationships might be expected to decrease with the combination of finer taxonomic groups that exhibit distinct environmental responses Figure 1c ; Supplementary Figure S1c. This scenario could happen if habitat preferences diverge quickly over evolutionary time; for instance, phylogenetically related species undergo adaptive diversification in habitat-use Schluter, ; Gavrilets and Losos, ; Pfennig and Pfennig, To test our approach, we used prokaryotic communities to evaluate the niche-based community assembly hypothesis here the niche is characterized by environmental conditions , as prokaryotes are functionally diverse and believed to be sensitive to the changes in environmental conditions, owing to large population sizes with short generation times Whitman et al.
Here, we used modern amplicon sequencing or shotgun sequencing of metagenomes Table 1 to determine prokaryotic community compositions. For prokaryotic identification, phylogenetic relatedness and taxonomic classification can be uniformly determined by a single genetic marker that is, the 16S ribosomal RNA rRNA gene Woese, , which permits a systematic comparison of prokaryotic compositions across various environments.
For hierarchical grouping of taxonomic units Yarza et al. In this study, we used both types of hierarchical systems to generate composition profiles at fine to broad taxonomic resolutions and evaluated how the strength of community—environment relationships changed along taxonomic ranks.
In addition, we calculated the phylogeny-based UniFrac metric Lozupone and Knight, as a standard to contrast our multi-level taxonomy-based composition analyses, as this metric has recently gained popularity in microbial community analyses and believed to be more informative than taxonomy-based estimates Lozupone and Knight, ; Swenson, Moreover, given the potential scale dependency Levin, ; Cavender-Bares et al.
Previous studies on plant communities focused on within-community phylogenetic structure have identified concerns about spatiotemporal and taxonomic scales on the detected pattern.
In this study, rather than detecting the within-community structure, we concentrated our analyses on the inter-community pattern that is, how taxonomic compositions differ across sites in response to environmental factors to assess whether changing taxonomic resolution affects the explained variance of community—environment relationships Figure 1. This analytical framework might be directly relevant to the question of how functional traits are conserved through macro-evolutionary time Lennon et al.
Moreover, plant ecologists have suggested that traits related to large-scale distributions that is, beta-niche; along temperature and precipitation gradients are generally conserved over time, whereas traits linked to small-scale distributions that is, alpha-niche; local coexisting patterns tend to be evolutionarily labile Cavender-Bares et al.
With this notion, we expect that the evidence for niche conservatism may be more easily detected by global data sets, whereas the evidence for niche divergence may be better revealed with local data sets.
Here, we used eight case studies Table 1 to demonstrate our approach and explore empirical patterns of prokaryotic community responses to environmental variables in soil and marine ecosystems. We argue that an approach such as ours, which assesses community—environment relationships while taking into consideration hierarchical taxonomic information, has the potential to advance our understanding regarding the evolutionary effects such as niche conservatism or niche divergence on community assembly, improving our ability to estimate community characteristics corresponding to environmental conditions.
We summarize the characteristics of these sequence-based prokaryotic community data sets in Table 1. Detailed information regarding data sources and data properties can be found in Supplementary Methods. The lists of community samples and environmental factors used in the present study are provided in Supplementary Tables S1—S8. To satisfy our requirement for accurate taxonomic assignments along fine to broad taxonomic resolutions, we adopted the classification system of the GreenGenes database providing a set of 16S rRNA gene references and linkage maps of reference sequences DeSantis et al.
Then, we used CopyRighter Angly et al. This initial site-by-otu97 matrix was then aggregated into a series of community matrices with fine to broad taxonomic resolutions, according to GreenGenes OTU linkage maps DeSantis et al. Two types of hierarchical community matrices were generated: 1 site-by-OTU matrices relying on sequence similarity; and 2 site-by-taxon matrices relying on database annotation.
For site-by-OTU matrices: the initial site-by-otu97 matrix was transformed into the site-by-otu94 matrix according to the GreenGenes otuotu94 linkage map DeSantis et al. For site-by-taxon matrices: hierarchical taxonomic annotation of the initial site-by-otu97 matrix was retrieved according to the GreenGenes explicit taxonomic ranks McDonald et al. Example files of site-by-OTU matrices, site-by-taxon matrices, and related R codes for community analyses along taxonomic ranks see below can be found in Supplementary Data.
To provide a reliable comparison in community structure across sampling sites, all the following community analyses were based on subsampled data sets with an equal number of sequence reads per community based on the minimum read number; Table 1. In addition, we applied abundance-weighted UniFrac unique fraction metric Lozupone and Knight, to quantify the phylogeny-based compositional variation, based on the site-by-otu97 matrix with the pruned GreenGenes phylogenetic tree containing only taxa found in each data set , using the QIIME platform Caporaso et al.
Before incorporating environmental measurements into community analyses, the similarity of the inter-community relationships that is, the Bray—Curtis results measured at different taxonomic resolutions was evaluated in order to estimate: 1 whether broad-level classification data can reveal inter-community relationships as fine-level classification data; and 2 whether two types of hierarchical systems site-by-OTU and site-by-taxon can generate comparable composition profiles from fine to broad levels.
To evaluate the similarity of inter-community relationships at different taxonomic resolutions, the compositional variation quantified at each taxonomic resolution was displayed in an ordination diagram using multi-dimensional scaling Legendre and Legendre, ; similarly, the phylogeny-based compositional variation that is, the UniFrac distance matrix was also displayed using multi-dimensional scaling ordination. To quantify the strength of community—environment relationships along taxonomic ranks, the explained variance adjusted R 2 Peres-Neto et al.
In order to equitably compare environmental influence on community structure at different taxonomic resolutions, we performed distance-based redundancy analysis with the whole set of environmental variables that is, the whole set of soil or water factors; standardized to zero mean and unit variance for each case.
Moreover, the analyses based on each environmental variable were also applied to assess the importance of each factor on community compositional variation. To further provide statistical assurance concerning application of our method, we conducted randomization tests to evaluate whether the detected trends of community—environment relationships along taxonomic ranks derived from the real data sets significantly differ from the predictions based on randomized data sets.
For multi-level taxonomy-based Bray—Curtis, we shuffled linkages between fine-taxa and broad-taxa level by level remaining topology , and generated randomized data sets based on random linkage maps. The explained variance of community—environment relationships for Bray—Curtis at each taxonomic level and phylogeny-based UniFrac generated from the randomized data were re-examined as described above as the null expectation.
The eight case studies used here cover distinct sampling scales and sequencing strategies Table 1 , allowing us to explore empirical patterns of community—environment relationships. In addition to the analyses mentioned above, as a variety of sequencing techniques with different sequencing efforts exist for modern sequence-based community data sets, we further conducted sensitivity tests to evaluate the effects of varying read length and read depth on our detected patterns along taxonomic ranks.
Basically, we found that the detected trends and strengths of the community—environment relationships along taxonomic ranks were robust, regardless of the sequencing length or depth. The detailed methods and results of these analyses are provided in Supplementary Methods and Supplementary Results. Moreover, although describing taxonomic composition with relative abundance information should be more precise when assessing community dynamics Jost, , however, for some early ecological surveys, only incidence data detection or non-detection of each species are available.
Thus, in addition to analyses based on abundance-weighted data, we also evaluated community—environment relationships based on transformed presence—absence matrices for the sake of comparison.
Compared with abundance-weighted results, the compositional variation explained by environmental factors was reduced markedly when using presence—absence data, although the detected trends along taxonomic ranks generally remained and showed a distinction from the randomization patterns. The detailed results can be found in Supplementary Results. In all eight cases, community dissimilarities in abundance-weighted compositions measured at distinct taxonomic resolutions are significantly correlated to each other Procrustes correlations ranging from 0.
These strong Procrustes correlations indicate that the inter-community relationships revealed by broadly resolved taxonomic data are largely consistent with the patterns revealed by finely resolved taxonomic data Supplementary Figures S2—S9. Specifically, measures at two adjacent taxonomic levels conveyed a higher similarity in their ordination diagrams than those at more separated levels Figure 2 ; Supplementary Figures S2—S9.
In addition, fine to broad levels of composition profiles extracted from two types of hierarchical matrices site-by-OTU and site-by-taxon were found to generally match Figure 2. Pairwise correlations of abundance-weighted compositions measured at different taxonomic resolutions based on Procrustes analyses. Measures of taxonomy-based compositions are calculated following two types of hierarchical classification sequence similarity: otu97 to otu70 and database annotation: species to phylum , and compared with the phylogeny-based composition using UniFrac metric.
Compositional variation at all taxonomic resolutions can be significantly explained by environmental factors for all of the eight case studies Figure 3 , suggesting that habitat filtering is a strong force determining prokaryotic community structure.
More importantly, the explained variation tends to increase or remain constant with broadening taxonomic resolution the red boxplots connected by the black line; Figure 3 , suggesting that taxa within the same lineages generally show similar responses to variability of environmental conditions.
For cases 1 to 4, which targeted surface soil prokaryotes, the explained variation gradually increased up to the class level otuotu76 , and then decreased at the phylum level Figures 3a—d. For cases 5 and 6, which represented seawater prokaryotic communities from two distinct epipelagic layers of the global oceans, the trends of explained variation achieved the maximum values at middle taxonomic levels, although the patterns differed in detail Figures 3e and f.
For case 7 of a seasonally sampled, temperate coastal, prokaryotic community dynamics, the explained variation remained constant from species to order levels, and then dropped at class and phylum levels Figure 3g. Whereas the explained variation for the prokaryotic community in subtropical shelf waters case 8 continually increased from species to the phylum level Figure 3h. Considering the effect of scale dependency, the habitat filtering implied by explained variation seems to be more evident in the two global soil cases than the two local soil cases, whereas this effect is unclear for the seawater cases.
The influence of environmental factors on variation of abundance-weighted compositions evaluated at fine to broad taxonomic resolutions. Taxonomy-based compositional variation is calculated following two types of hierarchical classification sequence similarity: otu97 to otu70 and database annotation: species to phylum , whereas phylogeny-based compositional variation is calculated using UniFrac metric for comparison.
The influence of environmental factors on composition adjusted R 2 , the explained variance is quantified by distance-based redundancy analysis db-RDA. The red boxplots connected by the black line along taxonomic ranks represent the explained variance of the db-RDA results based on subsamples derived from the original data sets, whereas the gray boxplots represent the results based on the randomized data sets.
The detected patterns derived from original data sets are significantly different from the expectation based on randomized data sets Figure 3. In the randomization null results the gray boxplots; Figure 3 , the explained variance remained constant or slightly decreased for community compositions at fine to broad taxonomic levels, with increasing uncertainty at broader levels that is, higher variability as indicated by the boxplots.
For soil cases, the amount of variation explained at the middle level genus to class was significantly higher in real data sets than what was expected from random. For seawater cases, the trends in explained variation along taxonomic ranks for real data sets were also significantly different from those predicted by randomization. Furthermore, considering the explained variation for the phylogeny-based composition UniFrac , it showed roughly the same degree as the family- or order-level estimates in original data sets, whereas it became similar to the species-level estimates in the randomization results Figure 3.
These findings indicate the importance of considering evolutionary history among species when surveying and evaluating the deterministic ecological processes acting on community composition.
Nevertheless, UniFrac, which weighs the phylogenetic information of the whole tree, may not necessarily be the optimal index to investigate environmental effects; that is, when to use UniFrac should depend on research purposes. For example, in the cases 5 and 7 Figures 3e and g , the explained variation for real UniFrac results was lower than expected by chance, as a phylogeny-based metric might function as a summary indicator, providing an average estimate for composition measures across all taxonomic levels Figures 2 and 3.
Overall, these results show that analyzing multi-level taxonomic data instead of focusing on a single taxonomic level can provide additional information regarding the impact of niche conservatism or niche divergence on observed macro-evolutionary patterns. Moreover, with respect to the results based on a single environmental variable Supplementary Figures S10—S17 , the strengthened or constant trends in explained variation of composition—environment relationships along taxonomic ranks are also evident in those cases, with pH accounting for the most compositional variation for soil samples cases 1 to 4; Supplementary Figures S10—S13 and temperature or nitrogen source accounting for the most compositional variation for seawater samples cases 5 to 8; Supplementary Figures S14—S Interestingly, for case 7, similar trends and strengths of composition—environment relationships can be detected when using day-length or artificial temporal indices as environmental variables Supplementary Figure S We examined prokaryotic communities in soil and seawater samples to evaluate whether niche-related signals remain, strengthen or vanish with changes in taxonomic resolution Figure 1.
We detected strong correlations among compositions at fine and broad taxonomic resolutions for all eight case studies Figure 2 and found an increasing or constant strength of the composition—environment relationships with broadening taxonomic resolution from species to order or even phylum level Figure 3.
These findings support the hypothesis of phylogenetic niche conservatism and further suggest that broader taxonomic classification may balance the distribution uncertainty associated with finer taxonomic units and strengthen niche-related signals. Overall, these results are consistent with the current notion of ecological coherence in deep prokaryotic branches Philippot et al. Typically, community data sets at the finest taxonomic resolution are preferred over more broadly resolved classifications in environmental assessment studies Terlizzi et al.
However, our results indicate that sometimes the use of broadly resolved data might be sufficient, or even superior to fine-level data if the goal is to identify community responses to environmental variables. Our assessment of environmental effects on the variation of prokaryotic communities detected a strengthened or constant niche-related signal with broadening taxonomic resolution Figure 3 , suggesting that phylogenetically related taxa might have constrained ecological properties and do not distribute randomly across habitats Andersson et al.
These results are consistent with some general notions about the phylogenetic conservatism of functional traits in microorganisms Martiny et al. For example, studies on soil bacteria and fungi have indicated that traits associated with moisture preferences are highly conserved, with a broad taxonomic level usually accounting for the greatest variation in a given trait Lennon et al.
Given strong phylogenetic conservatism, broadly resolved data may be well-suited for the detection and interpretation of prokaryotic community dynamics, especially in studies covering broad sampling scales with clear environmental variability. In addition to the spatial variation, the prokaryotic communities might be also sensitive to the seasonal variation, and this temporal dynamics might be also well represented by broadly resolved data.
In fact, the original studies of cases 7 and 8 have reported clear seasonal community dynamics using order-level or class-level compositions Gilbert et al. Prokaryotic communities in natural environments usually contain extremely high species-level diversity; however, the mechanisms maintaining such high diversity are still unclear Torsvik et al. Previous studies have hypothesized ecological equivalence for finely resolved taxa such that a particular niche may be occupied by any of the suitable taxa drawn from a large pool of candidates Lozupone et al.
Our results follow this line of thinking, and show that broader taxonomic units, which group closely related taxa as responding unions may enhance our ability to identify environment-driven community dynamics. Conversely, breaking down broader taxa into finer units may result in more noise than information, as the distributions and abundances of those ecologically equivalent units exhibit a high degree of randomness caused by neutral-stochastic processes instead of niche-deterministic processes Lozupone et al.
Actually, previous studies have indicated that despite the considerable variability in species-level composition, prokaryotic communities in a given habitat usually have stable phylum-level composition as well as similar functional attributes Fierer et al. It should be recognized that no particular taxonomic level is well suited for all cases.
Our findings for these prokaryotic case studies emphasize the profound value of broad-level taxonomic data when evaluating community—environment relationships, but do not imply that fine-level taxonomic data only capture or represent additional minor details. Rather, we highlight two considerations regarding community analyses at distinct taxonomic resolutions.
First, as discussed by other researchers Warwick, ; Bailey et al. For example, in studies of benthic invertebrates, broad-level taxonomic data have been demonstrated to be reliable when the objective is to evaluate the impact of pollution events on biological assemblages Warwick, ; Bailey et al. In contrast, fine-level taxonomic data are needed if we want to detect specific toxicological responses.
The only photosynthetic Protozoa are Euglenophyceae, which obtained their chloroplasts subsequently from an enslaved green alga [ 21 ]. The boundary between Protozoa and Chromista has been more controversial. Chromista was established to include all chromophyte algae those with chlorophyll c , not b considered to have evolved by symbiogenetic enslavement of another eukaryote a red alga as well as all heterotrophic protists descended from them by loss of photosynthesis or entire plastids [ 35 ].
With phylogenetic advances it has become clearer that alveolates once considered Protozoa are related to chromistan heterokont algae and related heterotrophic heterokonts and more distantly to Rhizaria, the three together forming the major group Harosa equivalent to SAR. Consequently, Chromista has been greatly expanded to include all Harosa as well as other former protozoa that turned out to be related to haptophytes or cryptophytes. Chromista now includes many groups once treated as Protozoa [ 19 ], an expansion followed here.
In multigene trees, this expansion is the most difficult part of the entire eukaryote tree to resolve. They sometimes show one or both of Plantae and Chromista as a clade but often their major subgroups are intermingled in contradictory ways [ 36 , 37 ].
This may be a consequence of the eukaryote-eukaryote chimaeric history of chromists that acquired some genes from red algae or of the very rapid basal radiation of the robust corticate clade i.
Plantae plus Chromista. Because of this, some question whether Chromista represents a clade, yet trees are still too poorly resolved to eliminate the likelihood from cell evolutionary considerations that Chromista and Plantae are genuinely distinct sister clades.
Evidence that Harosa is a clade is very strong. Evidence that Haptista plus Cryptista are a clade Hacrobia is strong on some trees but questioned by others [ 37 ]. Protozoa, like Prokaryota, is certainly a paraphyletic taxon [ 38 ]; Animalia, Fungi, Plantae, and Chromista all evolved from it.
In our hierarchy Protozoa comprises seven phyla, of which four are probably clades and three paraphyletic. We do not consider it useful in a general classification to subdivide the paraphyletic phyla into numerous smaller ones, often with only a handful of species that most have never heard of, even though a few specialists might favor that despite their constituent subgroups not differing radically in cell structure.
For both Protozoa and Chromista we have favored large groups with shared body plans, analogous to extremely diverse animal phyla like Chordata and Arthropoda. The higher proportion of ancestral paraphyletic phyla in Protozoa compared with terminal groups like animals and plants is unsurprising because they were the first eukaryotes and they diverged early on but with many fewer associated major changes in body plan than occurred during the much later radiation of bilateral animals.
Distinct early diverging protozoan clades can be remarkably similar morphologically and biologically [ 39 ]. As stated earlier, we take the view that the best demarcation between Protozoa and Fungi lies immediately before the origin of the chitinous wall around vegetative fungal cells and associated loss of phagotrophy. We use an updated version of the higher classification presented in the 10 th Edition of the Dictionary of Fungi [ 40 ].
The evolutionarily convergent Oomycetes such as the serious pest Phytophthora , formerly treated as Fungi, belong instead in phylum Pseudofungi of the heterokont Chromista. As with the other kingdoms, Plantae is classified in a variety of ways. Margulis and Schwartz [ 28 ] restricted Plantae to land plants embryophytes or higher plants and popularized the use of kingdom Protoctista to include lower plants green, red, and glaucophyte algae and lower Fungi as well as chromists with classical protozoa.
Many now consider such a kingdom too broad and heterogeneous and the associated separation of lower and higher plants in different kingdoms to be undesirable. Now taxonomists almost universally classify lower and higher plants together in the single kingdom Plantae and lower and higher fungi within the single kingdom Fungi. We have adopted this delimitation of Plantae here [ 19 , 35 ] for which Archaeplastida [ 12 , 18 ] is a less familiar recent synonym.
The structure of plastid genomes and the derived chloroplast protein-import machinery support a single origin of glaucophytes, red algae, green algae, and embryophytes land plants.
The ancestral embryophyte is thought to have originated from relatives of the Charales stoneworts or Coleochaetales Charophyta. Jeffrey [ 41 ] first grouped charophytes and embryophytes as a clade Streptophyta, which was later validated as a superphylum [ 42 ] and reduced to phylum by Bremer [ 43 ]. Here we recognize four embryophyte phyla—three of bryophytes liverworts, hornworts, and mosses and a single phylum Tracheophyta for vascular plants—with all species characterized by a diploid phase having xylem and phloem.
Bryophyte specialists tend to treat each of the three major bryophyte groups as phyla—Marchantiophyta, Anthocerotophyta, Bryophyta [ 45 , 46 ]. We have chosen a conservative approach to the higher classification of plants, largely consistent with Mabberley [ 47 ] for the embryophyte ranks above class, while using Chase and Reveal [ 44 ] and Stevens [ 48 ] for the lower ranks.
Based on the contributions of taxonomic experts to an outline of higher level classification and survey of taxonomic richness [ 60 , 61 ], as many as 39 animal phyla might be recognized more, if Porifera were abandoned as a phylum and constituent major clades given higher rank [ 62 ].
Below we discuss some issues encountered in arriving at decisions for our proposed classification, which accepts 34 animal phyla. Until the issue is resolved, we will follow the Porifera community [ 65 — 67 ] in retaining one phylum Porifera with four classes. Recent work on the vermiform myxozoan Buddenbrockia has demonstrated conclusively that myxozoans are extremely simplified Cnidaria, possibly Medusozoa [ 68 , 69 ].
We classify Myxozoa as a subphylum of Phylum Cnidaria. Egger et al. Whereas the stem-cell system and the mode of replacing epidermal cells unite both Acoela and Rhabditophora and are not found in any other bilaterian lineage, phylogenomic data support a separation of these two groups, a conclusion reached by Philippe et al. We follow Philippe et al. The remaining internal classification of Platyhelminthes is also somewhat problematic.
We propose a classification that is based in part on Riutort et al. Until recently, all four of these groups were commonly treated as separate phyla [ 28 , 61 , 76 — 80 ]. However, numerous recent molecular and morphological analyses nest Acanthocephala within Rotifera [ 81 — 86 ]. A syncytial epidermis links rotifers, Seison and Acanthocephala; Ahlrichs [ 87 , 88 ] proposed Syndermata for this clade. As revealed by transmission electron microscopy [ 89 ] and scanning electron microscopy [ 90 ], the jaw apparatus of gnathostomulids and rotifers is remarkably similar.
That of Seison is less obviously homologous [ 91 ] and the Seisonidea may have diverged from rotifers at an early stage of their evolution. On the other hand, Seison has similar sperm to acanthocephalans and the epidermis of both groups contains bundles of filaments. Limnognathia maerski , representing a new category of organism Micrognathozoa from cold fresh waters in Greenland and the Crozet Islands [ 92 , 93 ], has a remarkable jaw apparatus the most complicated known among invertebrates with clear homologies, in both the jaw elements and musculature, with the trophi in Rotifera and the jaws in Gnathostomulida.
The jaw apparatus and musculature, as well as molecular analyses, unite these taxa as a clade known as Gnathifera see [ 86 , 92 ]. In the analysis by Giribet et al. Edgecombe et al. We treat each of the major gnathiferan groups as a phylum, including Acanthocephala, following Monks and Richardson [ 79 ], though some of us think that the number of gnathiferan phyla ought to be substantially reduced when their phylogeny, including ingroup relationships of Rotifera sensu lato, is more firmly established.
The first three of these phyla have in common an eversible snout introvert with scalid spines and inner and outer retractor muscles, a similar excretory filter protonephridium , and similar sense organs, providing strong justification for uniting them in a single clade, the Scalidophora [ 97 ].
There is also molecular support, though not unanimity, for a clade of Kinorhyncha, Loricifera, and Priapula, known as Scalidophora. On the other hand, Kinorhyncha has internal and external body segmentation lacking in the other groups. Neuhaus and Higgins [ 98 ] noted that conflicting evidence exists for every one of the possible sister-group relationships among these phyla and prefer to keep them separate in a superphylum Scalidophora which is preferred over Cephalorhyncha, the latter name originally including the Nematomorpha.
We recommend separate scalidophoran phyla, though the number might be greatly reduced when the phylogeny becomes clearer. Some sequence analyses have questioned the monophyly of Chordata [ 99 , ]. Nielsen [ 95 ] maintains Urochordata or Tunicata and Cephalochordata as separate phyla, whereas the group Urochordata is closer to Vertebrata craniates , in a clade Olfactores, than Cephalochordata. We retain all three groups as traditional chordate subphyla.
They are abstractions. They are information storage boxes. Linnaeus was onto something when he proposed his hierarchical taxonomy, but he was too early to really grasp how lifeforms are related.
Indeed, they were: Linnaeus proposed that a kingdom of minerals Regnum Lapideum should sit alongside the kingdoms of plants Regnum Vegetabile and animals Regnum Animale. In in the 10th edition of his Systema Naturae , Carl Linnaeus established the fundamentals of the taxonomic system that persists to this day. These pages show his categorization of species within the animal kingdom, the Regnum Animale.
Carl Linnaeus. Individual characteristics may belong to multiple phyla, but the full set of characteristics is supposed to define each phylum uniquely. For example, among other defining traits, the anemones and jellyfish that make up the phylum Cnidaria are radially symmetrical, have an opening that serves as both mouth and anus, and capture prey with specialized stinging cells; the roundworms of the phylum Platyhelminthes have three distinct tissue layers as embryos, are bilaterally symmetrical, and lack a body cavity; the insects, spiders and crustaceans in the phylum Arthropoda have segmented exoskeletons and molt between developmental stages.
This idea that distinctive body plans could serve as an organizational scheme for life is actually older than the term phylum. The term phylum was coined by Ernst Haeckel in his Generelle Morphologie der Organismen , published in Over time, the number of animal phyla has expanded to about Yet there has never been a solid definition for what makes a group a phylum as opposed to a subphylum, a class or any other taxonomic rank.
He also noted that the distinctions are biased to favor human perspectives on what looks different, because they tend to emphasize qualities obvious to our eyes over less visible ones, such as genomic characteristics. But perhaps a bigger problem than the artificiality of the boundaries between phyla is that they also tell us little about the range in diversity within a phylum.
If it were part of an older lineage, Tetraplatia might be considered to belong in its own order, class or phylum rather than being lumped in as a bizarre hydrozoan beside the jellies, corals and anemones of the phylum Cnidaria. Or take rhizocephalan barnacles. The adult females are internal parasites of crabs: They grow inside their hosts in a form that resembles a branching mass of roots. They look about as different from other arthropods as you could imagine.
Indeed, the irony is that no matter what strange new forms evolution may invent in eons to come, no new phyla can be created to house them — because future organisms must fall under the same phylum as their ancestors, and the only firm taxonomic rule defining a phylum is that it cannot be nested inside another phylum. This points to the paradox inherent in the phylum concept: In theory, phyla mark the morphological uniqueness of distinct body plans. In reality, phyla are defined by more than body plans.
Some say that abrupt climatological or geological shifts were important, too — but whatever the exact trigger, the way that evolution altered species back then was seemingly different from the way it alters them now. But more recent data have countered this idea that there was something special about the diversification of life a half-billion years ago.
Their apparent morphological distance from one another could therefore be purely an artifact of fossilization and extinction, without being representative of unique biological processes.
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