## 1. Phylogenetic Inference in Biology


### 1.1 Primer and Introduction to Phylogenetics



A phylogeny is a reconstruction of evolutionary history. Thus the
discovery of evolution is a good starting point for the history of
phylogenetics. While Darwin was not the first to propose that some
species were genealogically related to others, it was *On The
Origin of Species* (Darwin 1859) that convinced many biologists to
accept common ancestry and to start building phylogenies. One of the
immediate--and ongoing--impacts was the question this raised
in how phylogenies (i.e., reconstructions of evolutionary history)
relate to taxonomies (i.e., how we group taxa).



![a phylogenetic tree: link to extended description below](Amemiya-2013-Fig1.jpg)

Figure 1 Phylogenetic (or evolutionary)
trees have become commonplace in biology research articles. This tree
displays a recent hypothesis on the relative placement of lungfish and
coelacanth in the evolutionary history of tetrapods (Amemiya et al.
2013: 312, fig. 1). [An
 extended description of figure 6
 is in the supplement.]




Pre-Darwinian taxonomy focused on classifying according to the
"natural system" where taxa were united into large groups
due to their "natural affinities" (Winsor 1976). In an
effort to clarify the concept of affinity, Richard Owen used the term
"homologue" to refer to "the same organ in different
animals under every variety of form and function" (Owen 1843).
Put into an evolutionary framework, the natural affinities uniting
these groups were regarded as the result of descent with modification
from common ancestors and "homology" has since become a
central term in comparative biology (Donoghue 1992; Hall 1994). To see
how homology relates to phylogenetic inference, as well as to
introduce some basic terminology, it will be useful to consider an
example phylogeny (Figure 1).



Figure 1
 provides a typical contemporary example of a *phylogenetic
tree*--a branching diagram that displays genealogical
relationships. Phylogenetic trees like these include branches (the
lines) and nodes (where the lines branch or come together, depending
on the direction you are reading the tree). Terminal branches are
marked with the entities whose evolutionary relationships are being
studied. In this case those entities are species or higher taxa, but
trees may be constructed for other taxonomic levels, or any other
biological entities whose evolutionary relationship can be studied
(e.g., viruses, stretches of DNA, extinct taxa). Phylogenetic methods
can even be employed to study ontogeny or cultural evolution, e.g.,
they have been used to construct cell fate maps to reveal how the
developmental parts of a single organism are related (Salipante &
Horwitz 2006), and used to reconstruct the expansion of language
families and help estimate historical human migration patterns (Gray
& Jordan 2000; Gray & Atkinson 2003; Gray, Drummond, &
Greenhill 2009). More recently, phylogenetic tools have been used to
aid epidemiology studies of COVID-19 (Lemieux et al. 2021).



Since so much of modern biology requires the ability to read
phylogenetic trees, good guides are commonplace. This includes journal
articles (e.g., Baum, Smith, & Donovan 2005; Baum 2008; Yang &
Rannala 2012), online guides (e.g., *Understanding Evolution*,
 Other Internet Resources),
 and more comprehensive textbooks (e.g., Felsenstein 2004; Lemey,
Salemi, & Vandamme [eds] 2009; Wiley & Lieberman 2011; Baum
& Smith 2013). Here, we provide only a brief introduction, but
recommend any of these (among others) to readers looking for a more
thorough and technical introduction.



On the phylogenetic tree in
 Figure 1,
 time passes from left to right with extant groups labeled on the
terminals of branches on the
 right.[1]
 Starting from the left there are two distinct branches, one leading
to Cartilaginous fish (sharks and rays), and the other to a node. From
that node the branches split, with one leading to Ray-finned fishes,
and the other leading to another, more recent node (that ultimately
leads to frogs, lizards, and mammals, among other taxa). Branches may
be read as representing ancestral lineages, with nodes representing
common ancestors that diverged (or split) into distinct lineal
branches.



We can also read the tree in
 Figure 1
 by starting with the groups on the right and moving leftward, tracing
branches back to where they join at nodes. This direction takes us
backwards in time, and allows us to read off which groups are more
closely related to others by looking at recency of common ancestry.
For example, lungfish are more closely related to frogs than they are
to pufferfish since lungfish and frogs share a more recent common
ancestor with each other than either does with the pufferfish. To see
this, trace back along the branches from each group, starting on the
right and moving left. You will converge on a node between the
lungfish and frogs before reaching the node between either of those
taxa and the pufferfish. Another way of saying this is that there is a
*monophyletic group* (or *clade*) that includes lungfish
and frogs that does not include pufferfish.



A monophyletic group consists of an ancestor and all of its
descendants. Some familiar groups turn out not to be monophyletic. For
example, if we tried to unite pufferfish and sharks into a single
group that excluded frogs and other tetrapods (such as the traditional
Pisces) we get what is called a *paraphyletic* group.
Individuals in paraphyletic groups all share a single common ancestor,
but exclude some descendants of that ancestor. Artificial groups like
"flying tetrapods" (birds plus the bats) would be called
*polyphyletic* since they have multiple, distinct origins.
Because paraphyletic and polyphyletic groups do not share a single
history that is unique just to them (i.e., there is no branch on the
tree that leads just to this group), they cannot feature in
explanations in the same way that monophyletic groups can, i.e., they
lack the utility and explanatory power of monophyletic groups (Velasco
2008a). For example, biologists might be interested in studying
whether flower diversity is driven by pollinator syndrome (i.e., moth,
bird, or bee) or vice-versa. Phylogenetic principles predict different
patterns of monophyly on these competing hypotheses, which can be
tested empirically and used by biologists as they design experiments
to test for the evidence of selection in cases like these (Whittall
& Hodges 2007). This is simply unavailable without the explanatory
and organizing principles of monophyly, and one reason it has grown to
dominate systematics.



Each node on a tree is the origin of a monophyletic group. For
example, the claim that the mammals form a single, united monophyletic
group just means that all and only mammals share (i.e., are descended
from) a common ancestor. We have very good reasons to believe that
this is true. For example, all and only the mammals have certain
traits (or characters) such as mammary glands, hair, and ossicles
(three bones in the middle ear). Each of these traits are
*homologues*, i.e., their similarity is due to shared ancestry.
(Homologous characters are typically contrasted with *analogous
characters*, i.e., traits whose similarity is not explained by
common ancestry, but by some other process, e.g., convergent evolution
or reversal). The mammary glands in humans and in elephants are
homologous because the trait has been inherited from their common
ancestor. In
 figure 1,
 the ancestor of the mammals is located at the node with branches
leading to the platypus and the rest of the mammals. Once traits
emerge (as evolutionary novelties) on a branch they can be passed down
(unchanged or modified) to the descendants of that lineage. Since
platypuses and mice have mammary glands, but no taxa stemming away
from earlier nodes do, we can infer that mammary glands evolved after
the node where mammals and birds split, but prior to the mammalian
node. That is one way phylogenetic trees support inferential claims in
biology. If mammary glands had evolved prior to the node joining
mammals and birds, then we might expect to find mammary glands on
birds or lizards. Platypuses and mice are also born inside an amniotic
sac, as are turkeys and lizards. However, frogs do not have an
amniotic sac. This tells us that the amniotic sac evolved after the
frog lineage branched off from the other tetrapods. Parallel reasoning
allows us to infer the history of the evolution of bones, tetrapod
limb structure, feathers, etc. These homologies form a nested
hierarchy of traits just as the monophyletic groups are nested, so,
for example, the mammals are a part of amniotes which themselves are a
part of the tetrapods. On the other hand, while the mammals and the
birds are both part of the amniotes, they do not overlap at all. And
indeed, no organisms have both mammary glands and feathers.



Explaining character distributions thus requires knowing which groups
are clades (i.e., monophyletic groups). Phylogenetic trees depict
precisely that, stating which groups are clades. This information is
called the *topology* of the tree, which conveys the relative
order of the nodes and nothing about their absolute dating in time.
Knowing which groups are clades allows us to reconstruct the evolution
of character traits, and it is the distribution of character traits
that is the basic way that we infer which groups are clades in the
first place. This style of reasoning (dubbed "reciprocal
illumination" by Willi Hennig (1966) led to charges of circular
reasoning from critics of phylogenetic inference (e.g., Sokal &
Sneath 1963), whereas advocates have sought to explain why this worry
is misplaced (see Wiley 1975; Sober 1988b, among many others). In
fact, the nested hierarchies of traits is what led systematists to
classify taxa into a nested hierarchy in the first place, and it was
the nested hierarchy of the taxonomic system that Darwin took to be
the most important evidence for common ancestry (Winsor 2009; Sober
2009, 2010). The problems of understanding what it means for traits to
be homologous and of inferring homology will be discussed later
 (SS2.1).
 Complicating things, groups of homologies do not always form
unambiguous nested hierarchies, and sorting out what phylogenetic
hypotheses are best supported in these cases is a big part of what
generates debates over phylogenetic inference (more on this in
 SS2
 and
 SS3.



Explaining character trait distributions is just the tip of the
iceberg. Phylogenies are centrally important for all research in
evolutionary biology. Phylogenetics lies at the heart of the linking
of the fields of
 systematics[2]
 and population genetics. Knowing a phylogeny is an important first
step to studying problems in evolutionary biology, functional
genetics, comparative anatomy, and evolutionary developmental biology.
Just as evolution is the unifying, organizing theme in biology,
phylogenetics is the backbone of biological inference more generally.
As Sterelny & Griffiths (1999: 379) put it, "Nothing in
biology makes sense except in the context of its place in
phylogeny".


### 1.2 From Darwin to Today: Three Interweaving Histories



For roughly 100 years after Darwin's *Origin*,
phylogenetic research in biology was common and important. Yet,
phylogenetics proceeded without much in the way of underlying theory
or explicable methodology--rather, the systematist with extensive
knowledge of some group simply relied on their judgment as to which
character traits looked genuinely homologous and which character
transformations seemed plausible. Some phylogenies were widely agreed
upon due to the fact that when homologies are clear, inference is
easy. As early as the late 1800s, there was a general consensus among
systematists around the branching order of many of the groups
presented in
 Figure 1.
 However, the relative placement of the coelacanth and lungfish was
disputed almost immediately upon discovery of the former in 1938
(Thomson 1991)--a dispute that has continued to draw attention
from biologists (e.g., Bockmann, De Carvalho, & De Carvalho 2013;
Amemiya et al. 2013; Takezaki & Nishihara 2017).



The lungfish and the coelacanth appear key to understanding the
origins of the tetrapods, which has remained a major question in the
reconstruction of evolutionary history since Darwin. But this is just
one example among many. Bowler (1996) surveys the development of
phylogenetics over this time period and examines a number of these
debates in more detail such as whether the arthropods form a
monophyletic group, and the question of the origin of birds, and, in
particular, how they are related to the extinct dinosaurs.



While there are still important disagreements regarding phylogenies,
the nature of these disputes have changed substantially as
phylogeneticists no longer rely largely on individual expert judgment.
The mid-twentieth century saw the introduction of competing accounts
of the theoretical foundations of systematics
 (SS1.2.1),
 a flurry of new algorithmic methods for constructing phylogenies and
taxonomic classifications, and the beginning of relatively easy access
to and employment of computational power
 (SS1.2.3),
 all combined with new sources of evidence in the form of various
kinds of molecular data
 (SS1.2.2).
 As biologists sought to incorporate these emerging features into
phylogenetic method and theory, debates arose over how (or whether)
this fundamentally changed the nature of systematics. At the same
time, debates about the foundations of systematics and the proper
methods for classification and taxonomy became highly prevalent and
interwoven with arguments about these new methods and newly available
data.



Traditionally, the emergence of phylogenetics has been presented
largely in the context of debates between three major schools of
twentieth century taxonomic thought, often called "The
Systematics Wars"
 (SS1.2.1).
 As centrally important as this was, a singular focus on that history
risks overshadowing the impact of the molecular revolution
 (SS1.2.2)
 and ever-increasing access to greater computing power
 (SS1.2.3)
 on the establishment of phylogenetics. Below we consider how each of
these twentieth century developments impacted the emergence of
phylogenetics as a distinct field of research. Yet, there are many
overlapping themes cross-cutting these developments (often reinforced
by allegiances between individual researchers), suggesting that,
ultimately, an integrated approach is needed for a rich, nuanced
history of phylogenetics.


#### 1.2.1 The Systematics Wars



Historically, phylogenetics emerged out of the larger field of
biological systematics, the field of biology that studies the
diversity of life and the relationships of living things through time.
Today, systematists typically treat "relatedness" solely
in terms of recency of common ancestry, but this was not always the
case. Pre-Darwinian taxonomists discussed the relationships of various
groups and their place in the "natural system", and while
the rise of evolutionary theory allowed that one sense of relatedness
was genealogical, it did not eliminate the idea of the broader notion.
Debates about the role of phylogeny in classification and taxonomy
were widespread (e.g., Huxley [ed.] 1940; Winsor 1995) though they
began to take on a new form beginning in the late 1950s as
collaborations turned into organized research programs pushing their
agendas.



In his analysis of the period, David Hull (1988: ch. 5) titled one of
his chapters "Systematists at War" and thus the name
"The Systematics Wars" is sometimes used to describe the
debates of the period. Hull (1970) influentially compared three
"contemporary systematic philosophies", typically called
*pheneticism*, (or *numerical taxonomy*, after the
seminal Sokal & Sneath (1963) textbook of the same name),
*cladistics* or *phylogenetic systematics* (after
Hennig's 1966 foundational text, an earlier version of which was
published in German in 1950), and *evolutionary systematics*
(following the lead of proponents like Simpson [1961] and Mayr [1969]
and others).



The pheneticists disputed that we were in a position to know common
ancestry with enough certainty to justify adopting it as foundational
to our notion of "relatedness", and maintained that the
taxonomists' job was discovering clusters of similarity (Sokal
& Sneath 1963). Pheneticists were critical of what they viewed as
the entrenched approach to systematics, arguing that the traditional
approach to selecting between hypotheses largely relied on the
relative prominence or status of the biologist rather than good
scientific methodology. They explicitly framed their views in the
context of arguments about what constituted "good science"
and sought to promote independent and objective modes of assessing
competing taxonomic groupings that relied on transparent and
repeatable methods. This included advocating for a greater separation
between biological theory and the construction of similarity
groups.



Around the same time, another group (phylogenetic systematists)
insisted that recency of common ancestry provided the best
theoretically-motivated foundation for relatedness (Hennig 1966). Like
the pheneticists, they too sought to promote good scientific methods
in systematics, and were similarly critical of established approaches.
The phylogenetic systematists can be characterized by their insistence
that all taxonomic groups above the species level should be
monophyletic. Thus, if birds are descended from dinosaurs then they
*are* dinosaurs (Padian & Horner 2002). If the dinosaurs
(or the reptiles more generally) are defined in a way that excludes
birds, then the group is paraphyletic and thus cannot be a taxon in a
phylogenetic taxonomic system. Ernst Mayr derisively named them
"cladists" for their obsessive focus on recovering clades
(i.e., monophyletic groups) (Mayr 1965a), though its practitioners
were happy to embrace the name. When consistently carried out,
phylogenetic taxonomic principles led to major revisions in
long-standing, traditional classifications--revisions that often
generated deep controversy (e.g., Halstead 1978; Gardiner et al.
1979).



Both pheneticism and cladism were developed in opposition to
entrenched views in systematics. In response, biologists committed to
more traditional methods sought to articulate the theoretical
underpinnings of their approach, which came to be known as
evolutionary systematics. With Ernst Mayr as their most prominent
advocate, evolutionary systematists sought to incorporate phylogeny
into classifications, but permitted that classifications might also
(or even instead) represent important adaptive differences between
groups (Simpson 1961; Mayr 1969). A prominent example that regularly
featured in these debates is the taxonomic placement of the birds and
crocodiles. Mayr argued that though birds are descended from dinosaurs
and reptiles more generally, birds share a cluster of adaptations
which justify classifying them as a distinctive group at a
taxonomically equivalent rank as reptiles. However, even though
crocodiles are more closely related to birds than to lizards and thus
belong to a clade with birds that lizards are not a part of, Mayr
still classifies crocodiles as part of the reptile
"grade"--a group characterized by a well integrated
adaptive complex (Mayr 1974). Thus unlike the cladists, evolutionary
systematists advocated for the inclusion of reptiles and other
paraphyletic groups in taxonomy.



One natural way to understand these debates is to recognize them as
systematists sorting out "reconstructing a phylogeny" and
"classifying a group of taxa" as two distinct tasks, and
working out how (if at all) these tasks ought to be reciprocally
informative (Mayr 1965a, 1974; Griffiths 1974; Hennig 1975; de Queiroz
& Gauthier 1990, 1992). With respect to this particular issue,
cladistics has largely won; today when scientists designate higher
taxa they are typically always describing monophyletic groups. The
principle of monophyly has proven to be such a powerful explanatory
and organizing tool that the commitment to it has grown widely and now
dominates systematics-in addition to informing both theory and
methodology across fields of biology This is one reason why this
period is sometimes referred to as "The Cladistic
Revolution" (Hull 1988; Kearney 2007; Haber 2009).



Yet, another framing is available. Namely, that as biologists shifted
to using phylogenies (rather than traditional taxonomies) to support
their explanations and justify their inferences, disagreements over
taxonomic classifications faded into the background. On this account,
it is disagreements over phylogenetic hypotheses that are impactful;
disputes over classifications become less consequential as they get
replaced in scientific inference by phylogenies. Joseph Felsenstein
(2004: 145) dubs this the "It-Doesn't-Matter-Very-Much
school", arguing that "systematists 'voted with
their feet' to establish this school, long before I announced
its existence". How phylogenetics ought to influence, inform, or
constrain taxonomic classifications is still a live debate (see
Ereshefsky 2001 as well as debates over the adoption of the PhyloCode,
among other debates about phylogenetic taxonomy), though one that is
orthogonal to the inference of phylogenies. Contemporary debates on
taxonomic classification are largely over how to draw inferences
*from* phylogenies *for* classifications; there is very
little dispute over the coherency of reconstructing phylogenies.
Biological classification, on this account, is not viewed as a
competitor to phylogenetics, but dependent on it--and not the
other way around. Phylogeneticists, like Felsenstein, can largely
proceed in reconstructing phylogenies without paying attention to
classificatory questions, though some of the metaphysical questions
about the units of phylogenetics can come back to impact the inference
of phylogeny itself (see
 SS2.6.2)



As an important aside, we say 'largely' in the previous
paragraph because--as in many cases in biology--there is not
unanimity. Notably, biologists that identify as *pattern
cladists* would dispute many of the chacterizations we have
offered about phylogenetics, including the very first sentence of this
entry, i.e., "a phylogeny is a reconstruction of evolutionary
history." Though there are a number of views that might be
identified with pattern cladism, all would typically regard that claim
as unjustifiably reading a process into what is largely a claim about
patterns only. On pattern cladism, phylogenetic trees are typically
regarded as graphical representations of evidence from characters; in
that regard, a phylogeny would just be a classification of those
characters. For the pattern cladist, directly inferring process or
evolutionary history from phylogenies--or, worse, building
evolutionary process assumptions into phylogenetic inference--is
a mistake. They defend this for numerous reasons, often on
philosophical grounds, i.e., that pattern cladism embodies good
scientific inference, or that evolutionary claims require additional
inferential steps that need to be made explicit (Farris 1983). This
has contributed to a split in the larger cladistics community. One
upshot of this process/pattern split is that the term
'cladist' has become ambiguous, contested, and imprecise,
and it is not always clear who gets regarded as what kind of cladist
(Carpenter 1987, The Editors 2016, Quinn 2017, Brower 2018a, Williams
and Ebach 2018). For example, while pattern cladists reject any *a
priori* assumptions about evolution (Brower 2019), so-called
*process* (or, sometimes, *phylogenetic*)
*cladists* regard evolutionary claims as foundational to
phylogenetics, while others (e.g., *statistical
phylogeneticists*) have distanced themselves from using
'cladist' as a label altogether (see
 SS2.3
 and the subsection
 SS2.3.1
 for more on this disputed taxonomy). We recommend that philosophers
writing in this area carefully specify the precise sense of
'cladist/m', if they use that term at all, and caution
against using it interchangeably with 'phylogenetics'.



Philosophers have largely focused on other topics in systematics than
the pattern/process split. This is somewhat surprising as there are
rich philosophical topics that could be fruitfully unpacked or drawn
on as data by philosophers of science, especially for those interested
in the nature of scientific explanation and inference, or the role of
theory in biological methodology. For foundational literature on
process versus pattern cladism see Beatty's (1982) introduction
of the designation of pattern cladism, with responses by Brady (1982),
Patterson (1982), and Platnick (1982). Two good recent pattern cladist
textbooks include Williams & Ebach (2020) and Brower & Schuh
(2021). There is an expansive though well contained literature within
cladistics featuring debates between pattern and process cladists,
e.g., Carpenter (1987), Brower (2000, 2002, 2019), and Lee (2002) are
just a very small selection of a large literature that philosophers
interested in the justification of scientific inference and methology
might explore.



Regardless of whether disputes about taxonomic philosophy died down
because systematists came to agree or because they became less
important, it is a mistake for philosophers to treat the Systematics
Wars as a live dispute, as opposed to an historical one. Yet, it
remains an important episode in the history of systematics, and the
core conceptual debates left their mark on contemporary debates in
phylogenetics (Haber 2009). At stake was not merely what notion of
"relatedness" systematists ought to adopt, but what
constituted good science in the context of inferring history and
constructing classifications, and the relative value (and even
coherence) of concepts like objectivity, testability, and
repeatability in science. But systematists no longer dispute
*whether* phylogenetic inference may be justified as a
scientific activity, but, rather, over how best to carry it out. A
large part of why this change took place has to do with the
availability of new sources of molecular data and the introduction of
new methods to take advantage of it. It is to this history that we now
turn.


#### 1.2.2 The Molecular Revolution



Like Felsenstein, Sterner and Lidgard (2018) encourage historians and
philosophers of biology to "move past the systematics
wars". Their point is not that this was unimportant, but that a
singular focus on it tends to overshadow the impact of other important
historical advances during this period. We agree. To remedy this,
Sterner and Lidgard suggest paying attention to the *practice*
of systematists in this period, rather than more narrowly focusing on
definitional disputes (embodying the so-called philosophy of science
in practice approach (Ankeny, Chang, Boumans, & Boon 2011)). Doing
so reveals that alongside the Systematics Wars were the dual
methodological revolutions of the mathematization of systematics and
the incorporation of molecular data. These trends cut across the
separate schools of systematics, generating their own set of
controversies. This is particularly acute in phylogenetics, where the
rise of the use of mathematical and computational tools was both
enthusiastically embraced and deeply resisted. We start with a short
description of the impact of molecular data on phylogenetics, before
moving to how mathematical and statistical tools were incorporated
into phylogenetic methods in
 section 1.2.3
 (for other histories on these topics see Dietrich 1994, 1998; Hagen
2001; Sterner & Lidgard 2018).



Let's first consider the appeal of including molecular data in
phylogenetic analyses--perhaps even to the exclusion of
morphological characters (e.g., Scotland, Olmstead, & Bennett 2003
though see Wiens 2004). One of the most important aspects of the
molecular revolution for phylogenetics was the sheer amount of new
data available to systematists, including for groups such as bacteria,
where fossil data were scarce or severely lacking. Yet it was not
merely the scale and availability of molecular data that held such
appeal. As it became easier to collect, code, and collate these data,
biologists argued that molecular phylogenetics provided an independent
source of evidence from morphological studies that bolstered evidence
for common ancestry and patterns of phylogeny (Zuckerkandl &
Pauling 1965). In contrast, more traditional systematists such as
Theodosius Dobzhanksy, Ernst Mayr and George Gaylord Simpson were
skeptical of molecular evolution studies quite generally, and, in
particular, the idea that they were in some way superior or could
replace morphological studies (Dietrich 1998).



Many of the earliest molecular studies typically supported established
phylogenies, e.g., Margoliash (1963) discusses the evolution of
cytochrome *c*, though simply presumes the received phylogeny
of species as correct. Later, Fitch & Margoliash (1967) construct
a phylogeny solely from the cytochrome *c* data, primarily to
demonstrate molecular phylogenetic methods and how to test the
reliability of results. Interestingly, one of the
"anomalies" they mention is that turtles are placed closer
to birds than to snakes--but this turns out to be no anomaly! The
vast majority of molecular studies have since placed the turtles as a
sister group to the archosaurs (a group which includes the birds and
the crocodiles) including the largest genome-scale studies done to
date (Crawford et al. 2015).



The case of the phylogeny of turtles as well as other recent molecular
studies (e.g., of the lungfish and the coelacanth, see Amemiya et al.
2013; Takezaki & Nishihara 2017) illustrate a general point. Even
in groups where we have extensive fossils and easily accessible living
specimens with detailed anatomical and morphological studies,
molecular evidence has often been taken to settle disputes and even to
overturn previously widely held views. This is in part due to the
sheer volume of molecular data, which can overwhelm other sources of
evidence, but also because of the way molecular data may be viewed as
evolving "neutrally", i.e., as the result of mutation and
genetic drift rather than natural selection (Duret 2008), and, thus,
be less prone to displaying *analogies* (the result of
convergent evolution), which can be mistaken for *homologies*
(the result of descent from common ancestry). (An analogy is the
similarity relation between two analogous characters, and is a kind of
"homoplasy" relation, i.e., characters whose similarity is
not due to descent from common ancestors.)



Analogies are false positives of evidence of common ancestry.
Morphological traits, it is presumed, are more subject to selective
pressures that can generate these false positives, and so molecular
data from sources less subject to selective pressures (i.e., neutral
sites) are preferable for reconstructing phylogenetic history. From
those phylogenies, analogies can be better identified, offering
evidence for appeals to natural selection (in the form of convergent
evolution or other evolutionary processes) as an explanation for the
pattern of biodiversity and trait distribution (Whittall & Hodges
2007 provides a good example of this reasoning in regard to pollinator
mode and morphology). Put another way, molecular data helps biologists
explain why what appear to be homologies fail to form nested
hierarchies. Molecular data can identify which of those might be
analogies instead, and this helps resolve what would otherwise appear
to be conflicts in phylogenetic inference. This is one way biologists
use phylogenies to offer data-driven, theoretically-grounded
evolutionary explanations, in contrast to "just-so"
stories.



In addition to adding more evidence in cases where systematists
already had a great deal of it, molecular studies can shed light on
cases where other sources of evidence are extremely weak. For example,
molecular data may preserve deep phylogenetic history otherwise
obscured at the morphological level by millions of years of adaptive
changes. Phylogeneticists depicting the very earliest branches in
plant and animal phylogenies, as well as relationships between the
different groups of eukaryotes, recognized how molecular data could
both test and enrich these deep phylogenetic hypotheses (Kenrick &
Crane 1997; Baldauf 2003). Subsequent molecular phylogentic analyses
have demonstrated the fruitfulness of using molecular data (e.g.,
amino acid sequences, RNA transcriptomes, and nuclear and
mitochondrial genomes) for constructing deep phylogeny hypotheses
(Heckman et al. 2001; Dunn, Giribet, Edgecombe, & Hejnol
2014).



Emile Zuckerkandl and Linus Pauling's introduction and use of
the hypothesis of a molecular clock provides a good example of the
powerful utility of molecular data and was an important early episode
in the history of molecular phylogenetics. The term "molecular
clock" was coined in Zuckerkandl & Pauling (1965), but they
had already used it without naming it in earlier publications
(Zuckerkandl & Pauling 1962; Pauling & Zuckerkandl 1963). The
basic idea is fairly simple. Given some estimated rate of molecular
evolution, the relative ages of branches and nodes may be estimated
given some set of molecular data mapped onto a phylogenetic topology.
Thus, molecular models of evolution were incorporated into
phylogenetic methods
 (SS1.2.3).



As more sequences of proteins became available, Zuckerkandl and
Pauling noted that homologous proteins displayed slight yet
systematically different sequences across taxa. For example, while
there were 18 amino acid differences between the human and horse
hemoglobin a-chains, there was thought to be only two
differences between gorillas and
 humans.[3]



Zuckerkandl and Pauling then assumed that amino acid changes were
roughly linearly proportional to time, and thus the time between the
divergence of humans and horses was nine times greater than that
between humans and gorillas. Using paleontological estimates for
calibration, Zuckerkandl and Pauling concluded that the humans and
gorillas diverged a mere 14.5 million years ago--much more recent
than traditionally was thought. This line of inference is not limited
to molecular phylogenetics, but has been imported for application to
other data types, e.g., similar reasoning from phylogenetic techniques
has been applied to language-tree divergence times to estimate
Indo-European and Polynesian human migration (Gray & Jordan 2000;
Gray & Atkinson 2003; Gray, Drummond, & Greenhill 2009). (For
more on the early development of the molecular clock, see Morgan
1998.)



Later, it was thought that the neutral theory of
 evolution[4]
 provided some theoretical justification for the molecular clock
hypothesis (Kimura 1968, 1969; Duret 2008), though Dietrich (1994)
argues that the relationship goes in the other direction with
Zuckerkandl and Pauling's research providing an essential
foundation for the development of the neutral theory in the first
place.



The molecular clock is but one of many examples of the importance of
molecular biology to phylogenetics. Importantly, incorporating
molecular data helped extend phylogenetic studies beyond eukaryotes,
i.e., the molecular revolution has fundamentally altered (or,
arguably, created) the field of microbial phylogenetics. Beginning in
1969, Carl Woese began to sequence ribosomal RNA (rRNA) from across
the prokaryotic kingdom in an effort to understand bacterial
phylogeny. The work was slow and difficult, but by 1975 Woese and his
lab had sequenced the rRNA of 27 different bacterial taxa (Woese et
al. 1975). In 1977, Woese and George Fox dropped a bombshell. They had
sequenced genes from a number of prokaryotic organisms whose rRNA
genes lacked the standard signatures of all previously known bacterial
rRNA genes and which appeared as distantly related to the bacteria as
did the eukaryotes. Woese and Fox named them the
"Archaebacteria" to emphasize their distinctness (Woese
& Fox 1977). In 1990, Woese, Kandler, and Wheelis (1990) produced
the first attempt at building a universal tree of life that included
bacteria, archaebacteria, and the eukaryotes based on molecular data,
with the project continuing to this day (Ciccarelli et al. 2006;
Hinchliff et al. 2015; Hug et al. 2016). All of this is only possible
because of molecular sequence data.


#### 1.2.3 The Mathematization of Phylogenetics and Introduction of Basic Phylogenetic Methods



The introduction of molecular data was highly significant in
phylogenetics. In addition to the amount of data it generated, and the
way molecular data provided evidence where other data were scarce,
this new type of data was also significant in how it promoted the
mathematization of phylogenetics. Molecular data lends itself to
mathematical and statistical analysis, in part due to (a) the ease by
which molecular data may be encoded; (b) the systematic way molecular
evolution may be mathematically modeled and subsequently incorporated
into statistical methods; and (c) the manner by which mathematical and
statistical tools help analyze the large volume of molecular data
under consideration. So though the molecular revolution and
mathematization of systematics need not go hand-in-hand, it is hardly
surprising that they do.



A number of authors have discussed the rise of mathematical thinking
and computational tools in systematics--especially in connection
with numerical taxonomy and phenetic classification (e.g., Hagen 2001;
Sterner & Lidgard 2014, 2018). Here we focus on the rise of
statistical methods for inferring phylogenies and later
 (SS2)
 we discuss the philosophical debates that arose regarding the use of
these methods. Good and more thorough textbook introductions for
phylogenetic methods include Felsenstein (2004), Lemey et al. (2009)
and Baum & Smith (2013). Yang and Rannala (2012) provide a helpful
recent review in the context of molecular phylogenetics.)



Incorporating molecular data both encourages and is facilitated by the
adoption of mathematical tools. We got a hint of that in the
discussion of molecular clocks
 (SS1.2.2),
 which use estimated rates of molecular evolution to estimate
divergence times. Around the same time, Anthony Edwards and Luca
Cavalli-Sforza--both students of R.A. Fisher--began thinking
of phylogenetic inference as a problem for statistics. After initially
working independently on the problem, they collaborated to produce
what are perhaps the first set of papers presenting phylogenetic
inference in a statistical framework (as attributed by (Felsenstein
2001, 2004); Edwards' own recollections of this work can be
found in Edwards (1996)).



Among the earliest algorithmic methods for inferring phylogenies are
those known as "distance" methods. "Distance"
could represent whichever character coding scheme we might be using to
construct the tree, molecular or morphological, e.g., the number of
nucleotide differences between two sampled sequences of DNA. A
distance method uses these pair-wise distances to infer the
phylogeny.



The first distance method developed is among the best justified
statistically, namely, the *least-squares method* of
Cavalli-Sforza and Edwards (1967). Here each topology has a set of
"best-fitting" branch lengths where fit is measured by
summing the squared differences between the expected distance and the
actual distance between each pair of taxa. The best tree topology is
the one whose best fit branch lengths are a better fit than any other
tree's best fit.



An alternative class of methods are known as parsimony methods. Here
we simply need a measure of how many steps it takes for one character
state to transform into another. Recall that different tree hypotheses
generate different explanations of the current character
distributions. The basic idea behind parsimony is that the tree
hypothesis that explains these character distributions with the fewest
number of evolutionary changes is to be preferred.



The first parsimony algorithm was published in Camin and Sokal (1965)
and a number of variants of parsimony have since been developed. For
example, one might want to weight certain kinds of changes relative to
others or even disallow certain kinds of changes (such as reversals)
all together. A survey of such methods as well as a collection of
different algorithms for carrying them out can be found in Felsenstein
(2004). Parsimony was the chosen method for the Cladistic school which
is part of the reason that the history sketched in "The
Systematics Wars"
 (SS1.2.1)
 above is intertwined with the history of mathematization sketched
here. Philosophical arguments for and against the superiority of
parsimony as an inference method will be discussed later in
 section 2.3.



If we think of phylogenetic inference as an instance of statistical
inference more generally, then likelihood and Bayesian methods spring
to mind as methods that exemplify more general, statistical paradigms.
Both require calculating probabilities and thus will require models of
evolutionary change i.e., some description of the process of evolution
and assignment of probabilities to possible changes in character
states (such as from one nucleotide to another). Let's briefly
walk through what that means, as how and whether to incorporate models
of evolution in phylogenetic inference has been a major topic of
debate.



If the data are observed nucleotide sequences from different groups of
terminal taxa, then the calculation we are after is the probability
that those observed sequences of nucleotides would arise on the tips
of competing phylogenetic hypotheses. The simplest way to calculate
that is to use the Jukes-Cantor model of evolution (Jukes & Cantor
1969). This model assumes an equal frequency of the four nucleotides,
and a uniform probability that any one nucleotide might change to
another. (These assumptions are typically violated, but reflecting
that in a model of evolution introduces additional parameters, which
increases computing time--a valuable and limited resource in
1969.) The Jukes-Cantor model provides a model of evolution on which
the relevant probability that any given sequence might have evolved
from another more primitive sequence may be calculated. Different tree
topologies will generate different probabilities that the observed
sequences evolved from a hypothetical ancestral state. This
conditional probability, \(P(\text{Data} | \text{Tree})\), is called
the likelihood of the tree.



Though the Jukes-Cantor model is still used, more sophisticated models
of evolution are available, e.g., the K80 (Kimura 1980), HKY85
(Hasegawa, Kishino, & Yano 1985) and Tamura-Nei (Tamura & Nei
1993) models of evolution. These introduce more parameters, e.g.,
assumptions about equal frequencies of nucleotides and/or the uniform
probability of changing nucleotide states may be relaxed and even
customized; models may also incorporate molecular clock
assumptions.



These parameter-rich models reflect a better understanding of
molecular evolution (e.g., that transversion and
 transition[5]
 rates between nucleotides are rarely equivalent) and the combination
of lowered costs and increasing accessibility to growing computational
power that permits more parameter rich analyses. The large number of
models available leads to an interesting statistical problem in its
own right known as the problem of model selection, i.e., which model
of evolution do we use in our phylogenetic tools? For discussions of
this problem in the context of phylogenetics, see (Posada &
Crandall 1998; Sullivan & Joyce 2005; Posada 2008).



Once we choose a model or collection of models to work with, we can
now calculate probabilities conditional on these additional parameters
(\(\theta\)). This likelihood may be formally represented as:



\[ L(\text{Tree}, \theta | \text{Data}) = P(\text{Data} | \text{Tree}, \theta) \]


As a reminder, the likelihood of a hypothesis given a set of data is
just equal to the conditional probability of those data given that
hypothesis. On these model-based phylogenetic approaches, the data are
conditional on both a particular phylogenetic tree hypothesis
*and* the stipulated parameters.



Using different values for the parameters in your model will lead to
different likelihoods for the tree. The maximum likelihood estimate is
the tree hypothesis along with the fitted parameters which maximizes
this likelihood. Felsenstein (1981) gave the first computationally
feasible algorithm for calculating likelihoods, and with modern
computing power combined with the huge amounts of sequence data
available, likelihood methods have become the most commonly used
methods in phylogenetics.



Just as with likelihood methods, Bayesian methods require a model of
evolution before any inferences are possible. The fundamental
difference is that likelihood inference treats parameters of the model
as nuisance parameters which have a fixed but unknown value. In the
frequentist method of maximum likelihood, these parameters are set to
their best fitting value. Bayesian inference treats parameters as
random variables with unknown distributions and probabilities are used
as a measure of uncertainty. The probability of a tree is a
conditional probability \(P(\text{Tree} | \text{Data})\) under the
assumption of a particular model. When we incorporate the model
parameters \(\theta\), by Bayes' theorem, we have:



\[P(\text{Tree}, \theta | \text{Data}) = \frac{P(\text{Data} | \text{Tree}, \theta)\*P(\text{Tree}) }{P(\text{Data})}.\]


Controversially, this requires that we attach prior probabilities to
the possible hypotheses as well as the parameters in our model.
Pickett and Randle (2005) claim that it is impossible to assign priors
in a consistent way. Velasco (2007) argues that this claim rests on a
mistake, though the problem remains a difficult one. He later
advocates assigning priors to tree topologies, but leaves open the
more difficult problem of priors on branch lengths or model parameters
(Velasco 2008b). Alfaro and Holder (2006) attempt to address some of
these issues.



Anthony Edwards (1970) was the first to discuss Bayesian methods in
phylogenetics, but it was computationally impossible to carry these
inferences out at the time. In the 1990s, three groups independently
developed methods for carrying out Bayesian inferences of phylogenies
in practice (Rannala & Yang 1996; Mau & Newton 1997; Yang
& Rannala 1997; Li, Pearl, & Doss 2000). Each group used
Bayesian Markov chain Monte Carlo (MCMC) algorithms to estimate
posterior probabilities, which other groups further refined (e.g.,
Larget & Simon 1999; Ane, Larget, Baum, Smith, & Rokas
2007; Huelsenbeck, Ane, Larget, & Ronquist 2008). (See
Archibald, Mort, & Crawford 2003 for an early and non-technical
primer on Bayesian phylogenetic methods.) With further statistical and
computational improvements, Bayesian methods are now fairly
commonplace in the literature alongside likelihood methods.


## 2. Phylogenetic Inference and Philosophy



Section 1
 served as an introduction to the history and problem of phylogenetic
inference. In this section, we will look at how phylogenetics and
philosophy are intertwined--in particular, we will examine some
foundational debates in phylogenetics that have a distinctly
philosophical flavor, and we will point out more traditional
philosophical questions that the study of phylogenetics can shed light
on.



At its core, phylogenetic inference is about evaluating competing
hypotheses. In an important sense, phylogeneticists are faced with
what philosophers of science would identify as a problem of
 underdetermination of theory by evidence
.
Multiple competing phylogenetic trees can explain the same data,
though in conflicting ways; it is the phylogeneticists task to
identify which of those hypotheses best explains those data. It will
be useful to separate the problem into two parts:


1. identifying what the evidence is; and
2. constructing a phylogeny to explain the evidence we have.



In the case of phylogenetic inference, we are in the philosophically
interesting and puzzling situation where it seems that these two
questions cannot really be separated.


### 2.1 Alignment and Character Coding



The question of which method of phylogenetic inference is best
justified is clearly important, but skips over an important step in
phylogenetic inference--the construction of a data matrix of
characters. Mishler (2005), for one, argues that it is the most
important step! Following Winther (2009), we can separate
*character analysis* which results in the construction of a
data matrix from *phylogenetic analysis* which treats the data
as input and produces a phylogeny. This first stage of character
analysis has been widely discussed in biology (though surely less
widely discussed than the second stage). In addition to the voluminous
literature on homology, useful collections of work on characters
include those edited by Wagner (2000) and Scotland & Pennington
(2000).



Character analysis includes identifying characters of the organisms we
are looking at, and in particular, identifying when a character in one
organism is the same character as in another organism. This is the
problem of homology. A different, but related problem, is assigning
character states. For example, one bear might have black fur while
another has white fur. Here, "fur" is the character, but
it is in a different state (fur color) in the two organisms. Though it
is standard to treat characters and character states separately, the
extent to which these are genuinely different problems rather than
than aspects of the same problem is a matter of debate (Stevens 2000;
Freudenstein 2005; Sereno 2005).



Identifying and coding characters is certainly a biological problem,
but it is a philosophical one as well. On what basis can we judge that
one character is homologous to another? Debates on this extend back to
the very beginning of contemporary biology, e.g., Richard Owen's
influential pre-Darwinian notion of homologue to Ernst Haeckel working
to develop accounts of homology in the context of Darwinian evolution.
In the mid-twentieth century, the pheneticists argued that character
identification must be "theory-free" and advocated using
raw similarity as a guide to identifying homology (Sokal & Sneath
1963; Sneath & Sokal 1973). There are well known philosophical and
biological problems with trying to use similarity in this way (e.g.,
Goodman 1972), many of which were raised as objections to strict
pheneticism during the Systematics Wars (e.g., Mayr 1965b; Neff 1986;
Rieppel & Kearney 2002 though see Lewens 2012 for a recent defense
of pheneticism in a contemporary context).



In a phylogenetic context, homologies are regarded as the result of
descent with modification from common ancestors. Similarity and
identity, in that context, is an expression of shared evolutionary
history. At the same time, homologies are also treated as hypotheses
from which we can infer phylogenetic hypotheses (and, subsequently,
both test and explain our hypotheses of homology). Clearly, even in a
contemporary phylogenetic context, questions remain on how to
individuate characters and identify homologies. Richards (2002, 2003)
worries that since there is no algorithm for individuating characters,
we rely on illegitimate, nonscientific factors rendering character
identification and phylogenetic analysis ultimately subjective.
Rieppel and Kearney (2007) and Winther (2009) respond that character
analysis can be considered objective since it relies on various kinds
of causal criteria; Kendig (2016) adopts a slightly different
strategy, rejecting the idea that we need to develop an analytic
definition of "homology", instead looking to biological
practice as a guide for individuating characters.



While character analysis and phylogenetic analysis are logically
separate tasks, in theory they should be intertwined. A given
phylogeny can make an assessment of homology more or less plausible.
One proposal to avoid this problem is the introduction of a
distinction between "primary" homologies (an initial
character identification based on structural features) and
"secondary" homologies (similarity due to common ancestry)
which are inferred through phylogenetic analysis (Pinna 1991; see
Roffe 2020 for a recent philosophical treatment of this distinction).
On the other hand, Rieppel and Kearney (2002) argue that character
hypotheses need to be independently testable and phylogenetic analysis
can clearly provide evidence for miscoding of characters--even if
this is not the intended goal.



Molecular data presents a different set of challenges for
individuating characters and coding them into a matrix, nicely
illustrated by DNA sequence data. With only four base nucleotides,
coding is easy
 (SS1.2.3),
 and modern laboratory techniques provide highly reliable sequences of
nucleotides in a stretch of DNA. But here, the problem is
*alignment*. How do we know that one stretch of nucleotide
sequences should correspond to similar stretches in other taxa? We
might consider chromosomal location, or even functional similarities,
though each of these raise other questions of how we decide what ought
to be treated as homologous characters (even contingently) across
taxa. This becomes even more complicated as we consider how historical
processes like gene duplication/loss or recombination add complexity
to these questions. This is the problem of positional homology. Like
morphological character coding, molecular alignment is intertwined
with phylogenetic analysis and various methods of joint inference have
been proposed (Redelings & Suchard 2005; Wheeler et al. 2006).


### 2.2 Computational Limits and Big Data



Bearing in mind what we said above about the interconnectedness of
character analysis and phylogenetic analysis, we will now proceed to
the second part, analysis of the data. At this stage, computational
limits loom large. Even if we are concerned only with inferring the
topology of a phylogeny, the number of possible trees grows
exponentially relative to the number of terminal branches we include
on a tree. The formula for calculating the number of possible tree
topologies is relatively straight-forward (Felsenstein 2004):



\[\frac{(2n-3)!}{2^{n-2}(n-2)!}\]


where *n* is the number of taxa. The upshot is that even a
small expansion of terminal branches results in an exponential
increase in the number of possible topologies. So, for example, there
are 15 possible phylogenetic trees for a group of 4 species, over 34
million for 10 species, and \(8 \times 10^{21}\) with 20
 species.[6]



This means that exhaustive evaluation of all possible hypotheses very
quickly becomes all but impossible--even as our capacity for this
has grown with increased computing power. Finding the most
parsimonious tree, the maximum likelihood tree, and even doing a
multiple sequence alignment to begin with are all NP-complete
 problems[7]
 (Graham & Foulds 1982; Wang & Jiang 1994; Chor & Tuller
2005). Bayesian inference is similarly computationally intractable
(Ronquist, Mark, & Huelsenbeck 2009).



In response, researchers have developed heuristic search strategies to
explore possible tree space. For example, in Bayesian analysis the
probability space of possible trees is so large that starting by
randomly selecting a tree will be highly inefficient. It will almost
surely fail to provide a good estimate of the posterior probability
distribution, since the interesting regions will only occupy a small
part of that distribution space (Ronquist et al. 2009). A common
heuristic is to start with a parsimony or distance-methods tree.
Though these methods typically perform worse than model-based
approaches, they are much less computationally expensive and can
quickly generate an initially plausible tree that Bayesian
phylogeneticists use as a launching off point to explore the tree
probability space (e.g., using hill-climbing algorithms such as Markov
Chain Monte Carlo (MCMC) sampling). Innovations like these are one
reason that computational phylogenetics is an important field in both
statistics and computer science. Philosophers of science interested in
how scientists explore and use so-called "Big Data" would
profit from examining how phylogeneticists have developed, adapted,
and employed heuristic search strategies.



Setting aside computational and practical problems leaves us with what
appears to be a straightforward epistemological question--which
methods are best justified in licensing phylogenetic inferences? We
begin our examination of this question by looking at arguments in
favor of a particular method, parsimony.


### 2.3 Justifications of Parsimony



Parsimony methods were among the very first adopted by cladists. This
is often taken to include Hennig (1966), who laid out a process of
phylogenetic inference that is now referred to as "Hennigian
reasoning" in his seminal text *Phylogenetic Systematics*
(Baum & Smith 2013). Like other forms of parsimony, this method
sought to identify the tree that explained the distribution of
homologies by appeal to the fewest number of evolutionary events.



Parsimony techniques have grown more sophisticated, and, as larger
data sets have been assessed and bigger trees constructed,
justifications of parsimony approaches advanced and adapted. Early on,
much of this took place in the context of the Systematics Wars
 (SS1.2.1),
 where a premium was placed on defending approaches as properly
scientific. Thus, these debates tended to veer into philosophy of
science, with systematists disputing what counted as doing good
science in the context of inferring history. This helps explain the
influence of
 Karl Popper's
 work

. Early
proponents of parsimony were attracted to Popper's
falsificationist account of the scientific method, both in the way it
provided demarcation criteria for scientific activity, and in the way
it was positioned as critical of
 logical positivist

approaches. Falsificationist justifications were employed to criticize
numerical taxonomists as verificationists relying on flawed
hypothetico-deductive methods.



Wiley (1975) provides a representative example of a falsificationist
defense of parsimony that proved deeply influential, e.g., Eldredge
& Cracraft (1980) adopt Wiley's core justification in their
influential textbook, as does Farris (1983) in his impactful defense
of parsimony. On Wiley's account, each taxonomic character is
treated as an homology, and considered independently of all others.
For each homology, a tree hypothesis is generated that requires the
fewest number of evolutionary changes to explain the pattern of
character distribution across taxa. These are hypotheses of homology
and, on the falsificationist account, these hypotheses are mutually
testable. That is, the tree hypotheses generated for each character
constitutes a test of the trees hypothesized for every other
character; falsified hypotheses of homology are treated as ad hoc
assumptions of homoplasy. The maximum parsimony tree is that topology
which generates the fewest falsified hypotheses of homology, and is
regarded as the most corroborated tree. Adding new characters or taxa
constitutes further testing of these hypotheses, and generates more
potential falsifiers. In this way, parsimony is simultaneously a mode
of tree construction and evaluation (Haber 2009); Wiley (1975),
Eldredge and Cracraft (1980), and Farris (1983) describe this as a
logical basis of reasoning about phylogenetic inference, citing the
philosophical justification of falsificationism.



Whether this represents a faithful account of Popper's
falsificationism, or whether falsificationism represents *the*
optimal (or even a desirable) mode of scientific reasoning has been a
matter taken up by philosophers (Hull 1983, 1999; Sober 1988b), though
that is, in some ways, beside the point. (For a philosophically-minded
biologist's survey of the role of Popper in systematics, see
Gaffney 1979, Rieppel 2008, or Santis 2020.) Regardless,
characterizing parsimony in falsificationist terms provided an
important justification that resonates to this day. More recent
invocations of Popper in phylogenetic inference have focused on
whether parsimony or model-based methods such as Maximum Likelihood
 (SS1.2.3)
 better align with falsificationism (e.g., Siddall & Kluge 1997;
de Queiroz & Poe 2001, 2003; Kluge 2001), and whether
probabilistic and statistical reasoning is relevant for studying
singular historical events (Siddall & Kluge 1997; Haber 2005).
(See Turner 2007; Cleland 2011;Currie 2018, 2019, for general
philosophy of historical science.) In
 section 2.4
 we describe how Felsenstein's (1978) discovery that parsimony
is prone to systematic errors poses a serious challenge to this
underlying falsificationist justification.



Parsimony can be defended without falsificationism, and other
justifications have been offered that appeal to the method's
namesake, i.e., the principle of parsimonious reasoning or appeals to
Ockham's Razor (e.g., Kluge 2005). It amounts to (i) defending
parsimony (the principle) as the criterion by which we ought to assess
competing phylogenetic hypotheses, and (ii) arguing that parsimony
(the method) is the best mode for achieving that goal by seeking out
that tree that minimizes the number of evolutionary novelties required
to explain the distribution of characters. This may also be presented
as identifying that tree that minimizes the number of ad hoc
assumptions of homoplasy. Though this appeal to simplicity may be
wedded to falsificationist justifications (as we saw above), they are
logically independent defenses. There is certainly an intuitive appeal
to the principle of parsimony in the sciences, and Ockham's
razor remains influential for good reason. But justifying parsimony
methods by appeal to simplicity is not so straight-forward. Evaluating
which phylogenetic approaches are justified by Ockham's Razor
depends on what is being counted (Haber 2009). Sober (1988b, 2015
among others) provide useful discussions of the role of simplicity and
the principle of parsimony in phylogenetics.



Other philosophical justifications of parsimony have been offered.
Fitzhugh (2008 and elsewhere) has argued that the problem of
phylogenetic inference is best understood as an abductive problem, and
that treating it as a problem for statistical (or inductive) inference
amounts to a category error. Sober (1988b) agrees that phylogenetics
involves abductive reasoning, though argues that this
*justifies* the use of statistical approaches (especially
likelihood).



It is also worth recalling why pattern cladists in particular object
to model-based methods of phylogeny reconstruction. Pattern cladists
typically reject *any* method of phylogeny reconstruction that
includes assumptions about the evolutionary process (SS1.2.1),
e.g., Brower (2019: 717) identifies a centrral commitment
distinguishing pattern from process (or traditional or phylogenetic)
cladism as the rejection of "*a priori* evolutionary
background assumptions from the inference of patterns of relationships
among taxa." As such, pattern cladists have strongly objected to
likelihood and other model-based phylogenetic methods, as those
explicitly incorporate evolutionary models (SS1.2.3 and
SS2.4). This has typically gone hand-in-hand with a rejection of
statistical approaches more generally, and a commitment to parsimony,
e.g., "the statistical approach to phylogenetic inference was
wrong from the start, for it rests on the idea that to study phylogeny
at all, one must first know in great detail how evolution has
proceeded. That cannot very well be the way in which scientific
knowledge is obtained" (Farris 1983: 17).



In response, statistical phylogeneticists have argued that parsimony
methods implicitly include evolutionary models, which model-based
methods simply make explicit and, thus, subject to interrogation (de
Queiroz & Poe 2001, 2003; Swofford & Sullivan 2009). This
fault line is further reflected in the way pattern cladists view other
proponents of parsimony as allies in this debate, as Brower (2019:
717), approvingly citing Kluge & Farris (1999), makes clear:
"despite this difference of opinion regarding critical
background knowledge, there is no practical distinction between
pattern and process cladists' evidence, analyses or results.
From a methodological perspective, all cladists group by synapomorphy
alone." That is, it is in the interpretation of methodology and
inferential justification that distinctions are drawn between pattern
and process cladists; in the larger debate with statistical
phylogeneticists they are allied. Note, however, that ever finer
distinctions of the term 'cladist' are being introduced
(let alone 'statistical phylogeneticists', who typically
associate with model-based approaches). This reflects the way this
term has grown contested and associated with various competing sets of
commitments. It is worth taking a brief detour in section 2.3.1 to
sort through this a bit.


#### 2.3.1 Who Owns the Term "Cladist"?



In
 section 1.2.1,
 we used the term "cladistics" to refer to a school of
taxonomy which held that higher taxa must be monophyletic, and stood
in contrast to the competing schools of phenetics/numerical taxonomy
and evolutionary taxonomy. In this historical context,
"cladistics" was sometimes used interchangeably with
"phylogenetics". We caution against that practice,
especially when discussing more contemporary practices. Though it is
hard to pin down a precise date when these terms began to carry
different connotations, Felsenstein (1978) and Beatty (1982) are as
good a marker as any for when these terms began to diverge and take on
more nuanced senses. This is particularly true of the term
"cladist", which has become a highly disputed term, and
one that has been claimed by sub-groups within the larger field of
phylogenetics.



An apt example of this is the journal *Cladistics*, whose
editors published a controversial editorial in 2016 stating that
submitted manuscripts should reflect the philosophical commitments of
the journal and its sponsoring professional society (*The Willi
Hennig Society*) (Editors 2016). They write,



> 
> 
> The epistemological paradigm of this journal is parsimony. There are
> strong philosophical arguments in support of parsimony versus other
> methods of phylogenetic inference (e.g., Farris 1983).
> 
> 
> 



The editorial continues, stipulating that "Phylogenetic data
sets submitted to this journal should be analysed using
parsimony". If trees generated by alternative methods show
different results, these may be included if authors prefer those
topologies, but only if authors are "prepared to defend it on
philosophical grounds" (all quotes from Editors 2016: 1). The
editors, here, are using the imprimatur of the journal
*Cladistics* to stake out a specific sense of how
"cladistics" ought to be understood, and what calling
oneself a "Cladist" signals about the underlying
theoretical and philosophical commitments held about phylogenetic
inference, i.e., an exclusive commitment to parsimony
methods--going so far as to reject model-based statistical
approaches entirely.



Aleta Quinn (2017) argues that the term "cladist" remains
one claimed by multiple overlapping (yet importantly distinct) camps.
She tackles the ambiguity head on, providing a useful "foreign
language dictionary" of ways that "cladist" gets
used in systematics. Quinn argues that the term is in need of
disambiguation, drawing seven different senses from the literature.
This includes systematists that advocate adherence to particular
phylogenetic methods (e.g., parsimony, as in the case of the
*Cladistics* editorial) or philosophical justifications
(especially to a version of Popperian falsificationism), though the
various distinctions of competing senses of "cladist" go
well beyond this. In response to Quinn, Williams and Ebach (2018)
propose an eighth (and their preferred) sense of cladist, and Brower
(2018a) offers a focused commentary on Quinn's article, as well
as other attempts to clarify the philosophical foundations and
commitments of pattern cladism. For example, a distinguishing feature
of pattern cladism is the rejection of *a priori* assumptions
about evolutionary theory in phylogenetic methods. This has been
interpreted as a claim of theory-neutrality or minimality by some
philosophers and biologists (e.g., Pearson 2010). Brower (2019)
contests this interpretation, arguing that the claim of theory
neutrality is specific to evolutionary assumptions, as opposed to a
more general claim.



The primary take away is that care should be taken when philosophers
use the term "cladist". Though there are a lot of
interesting philosophical topics to unpack around this term, it must
be acknowledged that it has become ambiguous. There are simply too
many senses of this term for it to be used without clearly specifying
precisely what sense is being invoked. We encourage philosophers
writing about phylogenetics to follow the lead of systematists, and
avoid using the terms "cladist", "cladistics",
and other cognates as synonymous with "phylogenetics".
Instead, philosophers should reserve usage of those terms and
carefully specify which sub-set of phylogeneticists (or their work)
those terms are referring to.


### 2.4 Long-Branch Attraction and model based methods



In
 section 2.3
 we mentioned that the falsificationist justification of parsimony was
met with a serious challenge by the discovery that it was subject to
making systematic errors under certain conditions. Joseph
Felsenstein's (1978) paper, "Cases in which Parsimony or
Compatibility Methods Will be Positively Misleading" first
describes what came to be known as the phenomenon of
"long-branch attraction". Though hardly the beginning of
debates between parsimony and model-based phylogenetic methods, it
represents an important inflection point in discussions about
phylogenetic inference.



Long-branch attraction refers to the tendency of some methods to
preferentially group together long branches (those with more
evolutionary changes) regardless of their actual history. Methods
designed to identify trees requiring the fewest number of evolutionary
changes to explain the data will prefer explanations of shared
ancestry over convergent evolution--even in cases where
convergent evolution occurred. For example, imagine two branches with
rapidly evolving DNA sequences. By chance alone, the two branches
might have the same mutation at the same site which parsimony treats
as evidence that these branches share a recent history. In a range of
cases, the chances of these similar mutations occurring is so high
that we expect the number of these convergent cases to overwhelm the
signal from the true homologies. In these cases, the parsimony method
is statistically inconsistent. A method is statistically consistent
when it is guaranteed, as more data are added, to identify the correct
solution. Methods that fail to do this are statistically inconsistent.
In this case, not only does parsimony fail to be consistent, but the
method reliably returns increased support for a specific, incorrect
outcome as more data are added and hence is "positively
misleading".



Long-branch attraction is more than just an operational problem, it
also challenges the falsificationist underpinnings of parsimony (Haber
2009). Recall that on Wiley's (1975) influential account,
phylogenetic trees are subject to being falsified as more data
(characters) are discovered or added
 (SS2.3).
 If a more parsimonious tree is available to explain those data, the
previously most parsimonious tree is treated as falsified (or as
containing more ad hoc hypotheses of homoplasy). Felsenstein's
discovery was that in some circumstances, as more characters are
included in a parsimony analysis, parsimony will be prone to rejecting
hypotheses that report the actual phylogenetic relations, while
corroborating more parsimonious--but incorrect--phylogenetic
hypotheses. This undermines the logical justification of parsimony
analysis.



Felsenstein (1978) was (and remains) an immensely impactful paper,
generating numerous research programs. It is a largely theoretical and
abstract paper that considers how different methods perform under
different biological conditions. One natural question that might be
posed concerns how prevalent these conditions are in the actual
systems being studied. This is an empirical question, of course, and
can be studied as such, though disputes over how to interpret
empirical cases have proven controversial (Huelsenbeck 1997; Whiting
1998; Siddall & Whiting 1999; Yang & Rannala 2012).



In the 1990s experimental phylogeneticists used empirical and
simulation studies to more rigorously empirically test claims of
performance of competing phylogenetic methods. This included testing
competing methods to reconstruct known in-vivo phylogenies (of
bacteriophage) that were carefully constructed, tracked, and archived
over many generations and more complex simulated phylogenies (taking
advantage of the ever-growing processing power available on the lab
bench) (Hillis, Bull, White, Badgett, & Molineux 1992; Hillis
& Bull 1993; Hillis, Huelsenbeck, & Cunningham 1994). These
largely confirmed that distance and parsimony methods were subject to
long-branch attraction, where model-based statistical methods such as
likelihood avoided these epistemic traps.


### 2.5 Phylogenetics & Philosophy of Statistics



Felsenstein's (1978) paper is also impactful because of his
commitment to statistical consistency as *the* standard bearer
for evaluating competing statistical approaches. For Felsenstein and
many other phylogeneticists, showing that parsimony is statistically
inconsistent is a fatal blow. But not everyone agrees. In fact, some
authors argue that it is close to an irrelevant consideration. We now
turn to this debate about the importance of consistency as an example
of how philosophy plays a central role in phylogenetic theory.



Like Edwards and Cavalli-Sforza before him, Felsenstein (1978) treats
phylogenetic inference as a problem for statistics. Jerzy Neyman
(1971) pointed to the emerging field of molecular phylogenetics as a
source of novel and interesting statistical problems. In the
intervening fifty years the field has only expanded in importance to
the point where phylogenetic inference is now one of the central
problems in statistics. Similarly, research on algorithms for
inferring phylogenies has been important in computer science for
decades.



If phylogenetic inference is seen as a problem in statistical
inference, it might be thought that general arguments for ideal
statistical methods will just apply in this case. Maximum likelihood
or Bayesian methods could be defended on these grounds for example. In
this light, parsimony is a statistical method and as such, we can
study its statistical properties (Felsenstein 1983, 2004). Anthony
Edwards (1996) claims that his initial usage of the "minimum
evolution" principle was as an approximation to the maximum
likelihood solution which he believed to be justified on general,
statistical grounds, i.e., parsimony methods could be assessed as a
statistical approach. Since that time, a number of authors have shown
exact connections (ordinally equivalent rankings) between parsimony
and likelihood in a range of cases (Felsenstein 1973; Sober 1985;
Tuffley & Steel 1997; Steel & Penny 2000).



In an exchange on parsimony and likelihood between Felsenstein and
Elliott Sober, Felsenstein defends the idea that consistency "is
a fundamental property" and in particular, that it is more
fundamental than likelihood (Felsenstein & Sober 1986). He
reiterates his earlier claim that



> 
> 
> maximum likelihood methods are not desirable in themselves, but
> because they have desirable statistical properties such as consistency
> and asymptotic efficiency. (Felsenstein 1978: 408)
> 
> 
> 



With this attitude, Felsenstein is merely following an old and
venerable position in statistics. For example, Fisher (1956: 141)
called consistency "the fundamental criterion of
estimation" and said that inconsistent estimators are
"outside the pale of decent usage" (Fisher 1935 [1950:
11]). Neyman (1952: 188) agrees.



Contrary to Felsenstein's position, Sober claims that



> 
> 
> One does not "justify" a method by showing that there is
> an extremely special case in which it does its work well; nor does one
> "refute" a method by showing that there is another special
> case in which it makes a hash of things. (Sober 1988b: 76)
> 
> 
> 



One particular argument that he gives against consistency is that it
can conflict with likelihood. Sober (1988a) presents an example in
which likelihood inference can fail to be consistent. But Sober
follows Anthony Edwards (1972) in positing that likelihood is a
"primitive postulate" which does not need justification
based on repeated sampling. If a likelihood inference can fail to be
statistically consistent but is still justified, then clearly
consistency cannot be a necessary criterion of justified
inference.



A different kind of worry about consistency is expressed by authors
who point out that it is not clear what consistent method we could
use. As the editors of *Cladistics* put it in their editorial
defending the use of parsimony,



> 
> 
> we do not consider the hypothetical problem of statistical
> inconsistency to constitute a philosophical argument for the rejection
> of parsimony. All phylogenetic methods, including parsimony, may
> produce inconsistent or otherwise inaccurate results for a given data
> set. (Editors 2016)
> 
> 
> 



Maximum likelihood estimation is known to be provably consistent under
a wide variety of conditions (Wald 1949), but several authors have
argued that these conditions do not apply to estimating the tree
topology since tree topologies are discrete, not continuous,
parameters (Yang 1996; Siddall 1998; Farris 1999). However, Swofford
et al. (2001) argue that Wald's conditions do apply and Yang
(1994), Chang (1996), and Rogers (2001) prove that maximum likelihood
is consistent under different assumptions about character evolution.
But regardless of which assumptions suffice for likelihood to be
consistent, some assumptions about the evolutionary process are
definitely required. But what happens when the model we use is not the
correct model? This is especially problematic when combined with the
view that to be a correct model it should be true, and that we could
never know that our model was true or perhaps even that there is no
such thing as a true model, as some pattern cladists have claimed
(e.g., Brower 2016, 2018b). As Felsenstein (2004: 272) puts it,



> 
> 
> likelihood is usually consistent if we use the correct model in our
> analysis. When we use the wrong model, there are few guarantees.
> 
> 
> 


### 2.6 Phylogenetics and Philosophy



The philosophy of statistics is but one area among many where
philosophical arguments contribute to work in phylogenetics. Though
phylogenetics is a subfield of biology (and, arguably, of statistics
and computer science), phylogenetic inference is a topic on which both
philosophers and biologists work, often in overlapping ways. Indeed,
the boundary between science and philosophy here is blurry in a way
reminiscent of some of the natural philosophy of the seventeenth and
eighteenth centuries. An upshot of this is that phylogenetic inference
is an enormously rich area for philosophers to explore. Biologists are
actively debating the conceptual foundations of phylogenetic
methodology, justification, goals, explanatory models, etc. It is not
uncommon to see philosophers playing a central role in these debates
(e.g., Hull 1988; Sober 1988b). It also means that many biologists are
eager to engage with philosophers in meaningful and deeply informed
ways (e.g., Sterner & Lidgard 2018; Havstad & Smith 2019).



Phylogenetic inference is, generally, best categorized as a problem of
epistemology. It is, after all, a problem of inference, albeit
specialized to how we justify or ground our beliefs about phylogeny.
An epistemologist who wonders how we can have knowledge of the past
can hardly do better than to look to phylogenetics (Sober 1988b). And
as we have seen, philosophy of statistics and of inductive inference
more generally looms large. But moving beyond epistemology, any
philosopher interested in how science works can learn from
phylogenetics. Here we will look at the cases of metaphysics and the
general philosophy of science.


#### 2.6.1 Phylogenetic inference as general philosophy of science



Understanding details of the evolutionary history of life is not
simply a stand-alone problem (as interesting as that problem would
be). The history of life and the history of the planet are tied
together such that knowledge in one domain aids our inferences in the
other. Knowing what types of fossils are embedded in rocks together
with a phylogeny can help scientists date the rocks just as dating the
rocks through independent means aids in dating the fossils and thus in
estimating aspects of the phylogeny (Grantham 2004). And just as the
physics and chemistry of the planet have shaped living things, life
has shaped the planet as well. Photosynthetic cyanobacteria were
partially responsible for the great oxygenation event roughly 2.4
billion years ago, where oxygen build up in the Earth's
atmosphere fundamentally altered the chemistry and physics of the
earth.



This entanglement with other sciences makes phylogenetics a rich
source for philosophers of science. It provides a model to better
understand how science works when specialized fields inform and rely
on each other. Philosophers might draw on phylogenetics to better
understand the social structure of complexes of sciences (Longino
1990, 2019) (and complex sciences that draw on other fields), or to
characterize what constitutes good scientific activity for what might
be called "service sciences", i.e., sciences whose
products are largely used by other scientists as end-users.



One way to frame debates in the Systematics Wars is as scientists
wrestling over the question of what constitutes good science. Here we
take the opportunity to highlight what may be an under-appreciated
aspect of this debate. Namely, does good science require theory?
Though many of the traditional sciences studied by philosophers of
science are clearly driven by (or, at least, tightly linked to)
theory, this does not always appear to be the case for other sciences.
Contrast Newtonian mechanics or population genetics with, say, the
study of stem cells (Fagan 2013) or developmental biology (Love 2014),
where it is unclear whether there are *any* underlying
theoretical structures.



In the case of systematics, it is unclear how to articulate a
*precise* theory for any of these competing views. It is
probably more accurately portrayed as competing sets of commitments,
some of which may resemble (or be) scientific theories (depending on
the characterizations of "scientific theory"), where other
commitments might be better described in terms of scientific practice
or mode of reasoning (Giere 1979 [1997]; Griesemer 2000).



Whether and what the theoretical structure of phylogenetics is remains
an open debate. Yet, though scientists that research, study, and
develop phylogenetic methods have a lot at stake in these debates,
most of the end *users* of these methods are rather agnostic
about those competing methods (and, in many cases, the underlying
theories supporting those methods). In practice, most biologists that
*use* phylogenetic methods typically use lots of them.
Philosophers of science might consider whether this amounts to an
adoption of methodological pluralism or theory agnosticism, and
whether trees produced under competing methodologies constitute robust
results (e.g., when identifying the same monophyletic groups or
homologies).



Phylogenetic inference should also be of interest to a wider range of
philosophers for the way it can function as a case study for other
philosophical issues. The recent history of phylogenetics has provided
philosophers with test cases for how scientists shift from one set of
commitments to another (Hull 1988), for the role of inductive logic
and probability in both explanation and model construction (Sober
1988a,b; Haber 2005), and for what constitutes a good scientific
explanation (Felsenstein & Sober 1986; Sober 2004; Haber 2009).
O'Hara (1988) and Ereshefsky (2012) look to phylogenetics to see
how narrative or historical explanations function in the sciences.
Other examples of philosophers using phylogenetics to assess
philosophical theses include Velasco (2012) and Vassend (2020) using
phylogenetics to examine practices of modeling and idealization, and
Haber (2005) looking at what phylogenetic inference tells us of how
probabilistic reasoning may be used to assess singular historical
events. Aleta Quinn proposes a taxonomy of disputes in contemporary
systematics that she argues stems from disputes over application of
the principle of total evidence (2019), and uses the case of
phylogenetics to examine a well known objection leveled against
proponents of inference to the best explanation in the sciences
(2016), the "best of the bad lot argument" (van Fraassen
1989), or the "underconsideration objection" (Lipton
2004). And finally, Andreasen (1998, 2000) looks to phylogenetics to
answer the question of whether biological races are real.


#### 2.6.2 Phylogenetic inference as a problem of metaphysics



Though phylogenetics as we described it is largely an epistemological
enterprise, biological systematics as a whole is filled with important
and controversial metaphysical issues which might seem to be largely
independent of problems of inference. Whether you treat taxa as
traditional natural kinds (Devitt 2008), homeostatic property clusters
(Boyd 1999; Wilson, Barker, & Brigandt 2007), individuals
(Ghiselin 1966, 1974; Hull 1976, 1978; Sober 1980; Ereshefsky 1991;
Haber 2016a) or something else (Slater 2013, 2014) will typically
carry little to no impact on debates about phylogenetic
inference--especially as those played out in the philosophical
literature.



Yet, early phylogeneticists certainly saw metaphysical implications of
their views, and this impacted how they approach the problem of
reconstructing phylogeny. Simply put, if you think that taxa are
historical entities, and that the aim of phylogeny is the
reconstruction of their histories, the task is very different than if
you take the aim to be to find ahistorical similarities or shared
essential characters (Hull 1970; Griffiths 1974). Though given the
ubiquitous adoption of the phylogenetic project (in a broad sense),
debates over how metaphysics ought to impact inferential methods
continues in some areas of phylogenetics.



Do competing metaphysical views in systematics impact phylogenetic
inference? Yes, though only obliquely and indirectly, and it's
very important to take care not to conflate issues of classification
with the problem of reconstructing phylogenies--even when they
overlap. Let's take a very brief look at the former, followed by
some commentary on the oblique and indirect way it may overlap with
phylogenetic inference.



Ereshefsky (2001) argues that a commitment to phylogenetic principles
entails a reformation of the practice and norms around biological
classification. Specifically, he recommends leaving behind the
traditional Linnaean classification naming practices in favor of a
phylogenetically informed practice. This follows a tradition stemming
back to the very earliest phylogenetic literature that sought to
reform biological classification around the principle of monophyly
(e.g., Hennig 1966; Griffiths 1976; Wiley 1981). For many
phylogeneticists this goal continues to this day--most recently
around the development of the PhyloCode (see
 Other Internet Resources;
 de Queiroz & Gauthier 1990, 1992, 1994; Cantino & de Queiroz
2020). Though structural reforms to wholly replace Linnaean taxonomy
have struggled to gain wide-scale traction and adoption, taxonomists
*have* largely adopted monophyly as a guiding principle in
classification and systematics--even in the context of so-called
traditional taxonomy (though microbial systematics has proven more
resistant; see
 SS3.1).



Of course, *how* biologists reconstruct a phylogeny and
identify monophyletic groups is a matter of taxonomic freedom on the
PhyloCode and other phylogenetic classifications. That's just to
say that phylogenetic inference will operate largely independently of
these metaphysical and classificatory issues--except insofar as
these classifications inform biologists about *what* it is that
they are aiming to reconstruct a history of in the first place. Haber
(2019) provides a framing for this challenge, arguing that the units
of phylogeny are the units of divergence and diversification. Where
Ereshefsky (2001) and other advocates of phylogenetic classification
provide a specific way of cashing out those units, Haber describes
this as a rich, general research project, similar to other
"unit" problems in philosophy of biology. In much the same
way that how we cash out, say, the units of selection may impact how
we study or draw inferences about evolutionary processes, how we
identify the units of divergence and diversification will impact how
we draw inferences in phylogenetics. (See de Queiroz 2007 for related
foundational issues.)



Padian & Horner (2002) offer a coarse-grained example of how a
metaphysical commitment overlaps with issues of phylogenetic inference
in their defense of treating Aves as the sole living branch of
dinosaurs. In this case, they identify "transformationist"
thinking against typological thinking. In the former, the identity of
taxa is associated with historical continuity, and untethered from the
expression of any intrinsic characters. Thus, the transformation of
ancient theropod dinosaurs into the the living clade of birds means
that birds *are* dinosaurs, and that this ought to be reflected
in our phylogenetic reconstructions. In contrast, they argue, a
typological account will treat birds as distinct from dinosaurs, and
risks a phylogenetic reconstruction that misrepresents the historical
relationship of these taxa. Moreover, they argue that
transformationist and typological approaches will differentially
categorize features of avian and ancient theropods as evidence and
data; what even counts as inferentially relevant is itself at stake.
Padian and Horner, here, are identifying metaphysical commitments from
phylogenetics to argue how paleontologists ought to reconstruct
evolutionary history, what counts as evidence, and what kinds of
evidence are important for drawing those inferences, i.e., how
characters are coded is treated as a methodological dispute entailed
by theoretical commitments. (See Haber 2016b in
 Other Internet Resources
 for further discussion.)


## 3. Looking Ahead: New Challenges and Opportunities



We began by noting that a phylogeny reconstructs evolutionary history
and that different types of entities have an evolutionary
history--for example, genes, organisms, populations, and species
all have genealogical histories. Assume that we are trying to
reconstruct the history of species or higher taxa (indeed,
"phylogeny" itself is sometimes defined this way, e.g.,
Wiley 1981; S. Edwards 2009). Standard methods such as parsimony,
likelihood, and bayesian methods all directly infer the history of
characters. So what is the connection between the history of these
characters and the phylogeny of the species? The unstated assumption
is that the histories of these characters are the same and line up
with the species history. We conclude this entry by acknowledging that
this unstated assumption is often violated and we consider what effect
this has on the theory and practice of phylogenetic inference. As we
will see, examining this unstated assumption forces us to directly
face the question of what a phylogeny actually represents and thus
what "phylogenetic inference" even means.



In order to facilitate an entry point into this topic we treated
phylogenetic trees and the histories they represent as simple
bifurcating entities, i.e., as straight-forward branch-and-node
structures. Yet, this is an idealization, and actual phylogenetic
histories are typically more complex than this. Branches can fuse
together, and branching events turn out to be more complex than the
unidimensional instantaneous point a node might suggest.



As philosophers of science are well aware, idealization in science is
both common and serves important purposes (e.g., Cartwright 1983;
McMullin 1985; Wimsatt 2007; Weisberg 2007). Yet, it can be fruitful
to pause and consider if some of these idealizations and simplifying
assumptions need to be re-examined, are hindering studies, or
generating other road blocks. Indeed, that's precisely what has
happened recently in phylogenetics. Re-examining the utility of
simplifying assumptions in phylogenetics has come both from outside
the field (particularly from microbiology) and internally (as
phylogeneticists began to appreciate what might be gained from
incorporating more complexity). This is (or, at least, can be) a good
and healthy thing for a science. It puts pressure on scientists and
philosophers to further articulate the central commitments of a
science, and to consider how to account for new challenges. Challenges
can also push a science to adapt and revise those central commitments
as new evidence, theories, and tools are discovered or developed, or
to revisit the utility of underlying simplifying assumptions and
idealizations. This, in turn, helps philosophers of science understand
how science can advance. (Haber 2019 describes this in the context of
phylogenetics as *productive disruptions*.)



Revisiting the utility of idealizing away the complexity of
phylogenetic histories has generated both hard challenges and new
opportunities for phylogeneticists moving forward (Haber 2012, 2019;
Velasco 2012). Here we provide a brief overview of what relaxing these
assumptions amounts to, and what kinds of complexity we are
acknowledging when these assumptions no longer hold. Following this,
we briefly describe how this impacts the central commitments and
project of phylogenetics.


### 3.1 Reticulation & Discordance



In the previous sections we treated phylogenetic trees as
straight-forwardly bifurcating models, regularly referring back to
 Figure 1.
 Branches represent ancestral lineages, and nodes represent lineage
splits. Yet, this view of phylogenetics is also highly idealized,
reflecting at least three simplifying assumptions: (1) that lineages
split but do not fuse; (2) that changes on one lineage do not affect
other lineages, and (3) that splits are singular, simple events. Each
of these assumptions turn out to be false.



In order to think about how lineages split or fuse, we have to first
answer the question of what a lineage represents. In the case of a
phylogeny where the tips are species, the natural answer is that the
lineages are tracking species through time and the nodes represent
speciation events. But consider the assumption that lineages split but
do not fuse. For many readers, hybridization may be the most obvious
counter-example. After all, hybridization is relatively familiar,
especially for cases involving sexually reproducing charismatic
mega-fauna and, even more frequently, plants.



In the case of speciation by hybridization, it is clear that entire
lineages can fuse. Exactly how often this occurs is disputed and of
course will depend on how you define species in the first place. But
it is a widespread occurrence in plants and is known to happen in
animals as well (Mallet 2007; Schumer, Rosenthal, & Andolfatto
2014).



While most occurrences of hybridization do not lead to new species,
many often lead to introgression-that is, the introduction of
genetic information from one lineage to another. If we model this as
one lineage connecting to another, we violate the assumption that a
phylogeny is a strictly branching tree diagram. But if we instead do
not depict these lineages as directly connected, then we violate
another background assumption in phylogenetics, namely, that changes
along one branch do not affect any other branches. In either case, we
now have a situation where the branching history of some characters is
not the same as the history of others or of the history of the species
as a whole. In the case of genetic history, this is known as
*genealogical discordance*.



While hybridization among sexually reproducing species already
provides good counter-examples to our assumptions about the
independence of lineages, the bigger challenges have come from
 microbiology
. More
specifically, what we find in microbial genomics are high levels of
horizontal (or lateral) gene transfer (HGT or LGT). For readers
unfamiliar with microbial genomics, this can be thought of as the
microbial analogue to hybridization, though there are important
qualitative and quantitative differences.



Qualitatively, HGT is not a mode of reproduction, though it does
involve the transfer of genomic parts between microbes. Unlike in
reproduction, the genomic transfer is not (typically) between parent
and offspring microbes, but adjacent ones. Roughly, when microbes make
contact, they can exchange parts of their genome, which means that the
history of these genomic parts may be distinct from the history of the
microbial organisms in which we find those genetic units. HGT can be
highly advantageous for microbes, and genomic parts can quickly spread
through microbial populations, especially if they carry beneficial
characteristics. Quantitatively, the rate of HGT in microbes is
enormously high and commonplace, especially relative to hybridization
in, say, mammalian species.



The upshot of widespread HGT is that microbial genes will frequently
have different histories, and genetic parts will sometimes flow from
one lineage to very distantly related lineages. Microbialists and
philosophers of biology have taken up the question of whether
reticulation in microbial histories is enough to entirely undercut the
utility of phylogenetics in microbial genomics, and how this has
impacted the utility of the *tree of life* metaphor more
generally (e.g., Martin 1999; Doolittle 1999; Velasco 2013 see too the
special issue of *Biology & Philosophy* [O'Malley,
Martin, & Dupre 2010]).



The final simplifying assumption treats lineage splits as singular
events that wholly separate a lineage into two distinct new branches
that we can cleanly track. When we think about the process of
speciation, it is clear that speciation and lineage separation more
generally is often not instantaneous (de Queiroz 1998; Harrison 1998).
What is less clear is how this matters to phylogenetics. Thinking
carefully about lineage splits and the internal structure of lineages
forces us to examine the nature and relationships of the multilevel
organization of the lineages we track (Haber 2012, 2019). More
inclusive lineages contain less-inclusive lineages as parts,
reflecting the hierarchical organization found in biology-a
feature of lineages well appreciated by early phylogeneticists, e.g.,
Hennig 1966; Griffiths 1974). Species trees, for example, contain gene
trees within their branches (Maddison 1997).



When individual gene histories are incongruent with each other or with
their containing species trees we have genealogical discordance. We
have already seen how hybridization and horizontal gene transfer can
cause discordance, but as systematics has become increasingly
connected with population genetics, another source of discordance has
loomed large--incomplete lineage sorting. Any two copies of a
gene in a population will share a common ancestor in the past called
their point of coalescence. If two gene lineages in the same species
lineage fail to coalesce within that species branch (that is, their
point of coalescence is earlier than than the most recent speciation
or lineage branching) we have what is called *incomplete lineage
sorting* (ILS). In a case of incomplete lineage sorting it is
possible that one of the gene lineages first coalesces with a gene
lineage from another species and thus is a case of genealogical
discordance. This generates what Avise and Robinson (2008) have termed
*hemiplasies*, i.e., homologous characters whose phylogenetic
histories are topologically discordant with higher level phylogenies
due to ILS. Maddison (1997) provides a very readable introduction to
the relationship between gene trees and species trees and the problems
of genealogical discordance while Degnan and Rosenberg (2009) is a
slightly more advanced introduction to the biology of incomplete
lineage sorting and its affects on phylogenetic inference.



To summarize, we first discussed phylogenetic inference as it was
typically done prior to around the 1990s. First, infer the history of
a number of characters (for example, genes). Each of these histories
is a particular, branching tree. Now assume that these histories are
the same as the species history. It is this assumption that allows
methods such as parsimony or likelihood to evaluate competing
hypotheses. The species history is then also a branching tree even
though we know that in general, this is a kind of idealization
assuming that (1) that lineages split but do not fuse, (2) that
information is not transferred between lineages , and (3) that splits
are singular, simple events. Each of these assumptions turn out to be
false.


### 3.2 Impacts on Phylogenetic Inference



The reticulation and genealogical discordance generated by
hybridization, HGT, ILS, and other complexities stemming from the
structure and processes involving biological lineages have,
historically, been obscured by idealizing assumptions in
phylogenetics. In many cases treating phylogenetic trees as idealized
models of genealogical history is useful and appropriate for the
research question at hand.
 Figure 1
 offers a good example of this. This reflects how the level of
resolution relative to our research questions can drive the way we use
phylogenetic tools (O'Hara 1993).



But what happens when these idealizing assumptions are critically
interrogated? First, the fact that different entities have different,
often incompatible, genealogical histories brings into question the
very nature of phylogeny. One line of thought is that a phylogeny
should directly track genetic history rather than the history of
speciation. As Maddison (1995: 285) puts it,



> 
> 
> one possible interpretation of a species phylogeny is that it depicts
> the lines by which genetic information was passed on and nothing
> more.
> 
> 
> 



Baum and Shaw (1995), Baum (2007), and Velasco (2010, 2019) represent
a series of papers devoted to developing this "concordance
tree" idea. Baum (2009) incorporates this line of thought about
phylogenetic history into the debate over the nature of species and
other taxa.



Philosophers have paid particular attention to reticulation in the
context of microbial genomics, and for good reason (O'Malley et
al. 2010 is a notable special edition dedicated to this topic and is a
useful resource). These are often framed as challenges to the
phylogenetic approach itself, or, in other cases, how and whether the
tools of phylogenetics apply to microbial genomics (Martin 1999;
Doolittle 1999). The breadth of HGT in microbial systems has also led
to challenges to the more conceptual and metaphysical commitments
about species and other phylogenetic units (Franklin 2007).



However, the majority of work in phylogenetics today continues with
the same goals as before, but with biologists using better and more
powerful tools for incorporating these complexities into their
phylogenetic models. Haber (2019) offers a philosophical treatment of
how reticulation and genealogical discordance introduces both
challenges and opportunities in the context of phylogenetic inference,
asking whether the branches of phylogenetic trees are "too
thin" and obscure relevant details found in the internal
structure of those lineages. There are a lot of good reasons
phylogeneticists might aim to include reticulate structure in their
phylogenetic reconstructions. Among other things, it means treating
genealogical discordance as data that can be mined, as opposed to
noise that needs to be filtered out (Haber 2012, 2019). Philosophers
have begun paying more attention to ways that reticulation and
discordance have impacted phylogenetic inference beyond microbial
genomics. Rather than coming as challenges from cognate fields, these
include ways that practicing phylogeneticists have sought to account
for and accommodate what we have come to understand about reticulation
and discordance (see Haber & Molter 2019 for a special issue
dedicated to this topic).



Population biologists were among the first to operationalize methods
for extracting information about the history of lineages from the
internal structure of those lineages, i.e., by examining the structure
of individuals in a populations, population biologists developed
methods for inferring historical coalescent points of those
individuals (Hudson 1983; Kingman 1982, 2000; Tajima 1983). These
methods exploit the fact that after a lineage splits it takes time for
that split to be wholly reflected in the kinship relations of the
parts of those lineages. Or, to put it in more familiar terms, it
takes time for the parts of a lineage to all be more closely related
to each other than any are to parts in other lineages These approaches
can be viewed as the continued development of methods in the larger
context of the close ties between the application of statistical tools
and molecular biology
 (SS1.2.3).



Maddison (1997) represents a more recent launching point for a new
appreciation of the role that the internal structure of lineages can
play in phylogenetic inference. Briefly, the continued advancement of
molecular and computational techniques provided the opportunity to
produce ever-finely resolved phylogenies of the systems of interest.
What quickly became apparent was a confirmation of what is only
implicit in Hennig (1966): that the hierarchical structure of taxa may
result in discordant phylogenies at different levels (or even within
levels) of the biological hierarchy. As an example, Maddison describes
how the phylogenies of individual genes or nucleotides may display
different phylogenetic topographies from the organisms or populations
in which we sampled those genes. This is not a result of error or
homoplasy, but of genuine phylogenetic discordance between gene level
lineages and the higher-level lineages in which they reside.



Maddison (1997) is also directly about how we ought to incorporate
what we learn about the internal structure of lineages into our models
of phylogenetic inference, and how we might build on the earlier work
on coalescent models. Maddison is among the first to deeply appreciate
how impactful the then-new work on genomics might be for
phylogenetics, both in the ways that discordance will present serious
challenges and opportunities for phylogenetic inference. This has
helped spur new developments in this field of phylogenetics, (e.g.,
Maddison & Knowles 2006; Degnan & Rosenberg 2006, 2009; Baum
2007; Nakhleh 2013; Hahn & Nakhleh 2016; Mallet, Besansky, &
Hahn 2016).



Haber (2019) offers two reasons why these new developments in
phylogenetic inference should be of interest to philosophers. First,
he offers a real-time example of scientists shifting from treating a
feature of a system as noise to recognizing it as powerful evidence.
Pease et al. (2016) is a prime example of this. They use some of the
new tools developed to draw phylogenetic inference in highly
discordant systems to offer a reconstruction of the tomato clade.
Where earlier analyses regarded the system as too noisy to pull out a
strong phylogenetic signal, these new models permitted a
reconstruction that recognized specific patterns of discordance. This
represents a substantial advancement in the level of resolution
phylogeneticists are able to reconstruct and identify.



Second, the new attitudes toward discordance and reticulation in
phylogenetics provide an example of a community of scientists shifting
from one set of commitments to another. Haber (2019) offers a specific
example. He describes one of the entrenched core commitments in
phylogenetic methodology as being the goal of "resolving"
competing phylogenetic trees (Felsenstein 1988). That is, in cases
where different analyses or data are generating conflicting
phylogenetic trees, the goal should be to resolve this conflict by
identifying which of those trees is best supported. On a model that
recognizes genealogical discordance, though, conflicting trees may
accurately reflect a reticulate or discordant phylogenetic history,
e.g., if different data strongly support different phylogenetic trees,
one possible explanation is that we have discovered an instance of
discordance. Nakhleh (2013) recommends that this entails that
phylogeneticists must now also consider whether conflicting trees may
be "reconciled" rather than resolved. Haber (2019) argues
that this is an example of what he calls a "productive
disruption", which is one way that science might advance.



The conceptual and epistemological impacts of discordance on
phylogenetic inference come together in recent debates over Sukumaran
and Knowles' (2017) work and commentary on the multispecies
coalescent (Rannala & Yang 2003; Yang & Rannala 2010). Very
roughly, the multispecies coalescent extends the principle of the
coalescent from population genetics to phylogenetics. In population
genetics, a gene can be sampled from multiple individuals in a single
population; coalescent theory provides a way to model the ancestral
history of that gene to a common ancestor, i.e., where that history
coalesces. The multispecies coalescent does something similar, but
with samples from multiple populations (or even species). These and
other techniques were developed in response to the challenge posed by
Maddison (1997) to develop methods that recognize speciation as an
extended process. Roughly, the lineages of genes, individual
organisms, populations, and species within a taxa can all "come
apart", or have different phylogenetic histories, and display
"a fractal hierarchy of divergences" (Sukumaran &
Knowles 2017: 1607). Multispecies coalescent techniques help reveal
the fine-grained structure of complex multi-level lineages (Haber
2012), which has generated discussion on how these internal lineage
structures relate to species delimitation and other phylogenetic
inferences (e.g., de Queiroz 2007; Knowles & Carstens 2007; Haber
2012; Carstens, Pelletier, Reid, & Satler 2013; Hahn & Nakhleh
2016; Mallet et al. 2016; Velasco 2019).



Sukumaran and Knowles (2017) provide a good example of the discussions
over what kinds of inferences are licensed by the multispecies
coalescent, and the way that metaphysical concerns overlap with
epistemological ones. They focus on what kinds of inferences may be
drawn from multispecies coalescent methods, arguing that it is a
mistake to simply treat the units identified by these methods as
species, when they may turn out to be population-level structures
instead (i.e., populations of a species, rather than distinct
species). They seek to highlight that mistaking population structures
for species (or too quickly drawing the inference of the latter from
the former, without further evidence), can undermine precisely the
sorts of inferences that phylogenetics seeks to license by
artificially and unjustifiably inflating species counts:



> 
> 
> Specifically, all fields that rely on species as units of analysis,
> from conservation biology to studies of macroevolutionary dynamics,
> will be impacted by inflated estimates of the number of species,
> especially as genomic resources provide unprecedented power for
> detecting increasingly finer-scaled genetic structure under the
> multispecies coalescent. (Sukumaran & Knowles 2017: 1607)
> 
> 
> 



This may be framed as a claim about the units of divergence and
diversification being identified by these multispecies coalescent
methods (a metaphysical claim), and the sorts of inferences that may
be drawn about those units from these methods (an epistemological
claim) (Haber 2019).



To be clear, the relation between the metaphysics and epistemology is
not direct, but oblique, yet important in regard to the inferences
licensed by phylogenetic methods. Sukumaran and Knowles (2017), for
example, conclude by issuing a plea for phylogeneticists to exercise a
more careful stance towards relying on genomic data alone, and in
using multispecies coalescent as a straight-forward way to identify
species. They instead propose a more cautious inferential stance,
identifying the units identified by the multispecies coalescent as
population-level structures (which may or may not correspond to
species delimitation). This is merely an example of the rich sorts of
discussions around good scientific inference that is available to
philosophers willing to dive into this literature.