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Abstract

In this paper, I show that population geneticists are acknowledging a kind of biological population that has hitherto been unappreciated by philosophers. The new population talk occurs when population geneticists call continent-level human genetic clusters 'populations' in population structure research. My theory is that the kind of population being referred to is the K population, which is, roughly, a biological population whose members are united by common genomic ancestry and population membership is graded. After presenting and defending the theory, I show that the K population is indeed a kind of biological population. Finally, I address likely objections.
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Do Humans Have Continental Populations?
Abstract. In this paper, I show that population geneticists are acknowledging a kind
of biological population that has hitherto been unappreciated by philosophers. The
new population talk occurs when population geneticists call continent-level human
genetic clusters ‘populations’ in population structure research. My theory is that
the kind of population being referred to is the K population, which is, roughly, a
biological population whose members are united by common genomic ancestry and
population membership is graded. After presenting and defending the theory, I
show that the K population is indeed a kind of biological population. Finally, I
address likely objections.
1. Introduction
There is a recent trend among population geneticists to call the continent-level human
genetic clusters inferred from structure-like algorithms ‘populations’.
1
For example, Michael
Bamshad et al. (2003, 584-585) call Africans, East Asians, Europeans, and Indians “continental
populations” when conducting population assignment with Alu insertions and microsatellites.
Brian McEvoy et al. (2010, 297) call Africans, East Asians, Eurasians, Native Americans, and
Oceanians “geographic populations” when using frappe and structure to detect human population
structure. Marcus Feldman and Richard Lewontin (2008, 90) call these same continental groups
“geographical populations” when summarizing two landmark studies on human population
structure. Furthermore, Lev Zhivotovsky, Noah Rosenberg, and Marcus Feldman (2003, 1183)
call both East Asians and Eurasians “a metapopulation” when doing human genetic clustering
research. However, this linguistic pattern raises two philosophically interesting questions.
First, what kind of population are population geneticists referring to when they call
continent-level human genetic clusters ‘populations’?
2
Second, is the kind of population being
referred to a biological population? The second question requires some clarification. Philosophers
who study populations in biology agree that there are many ways that biologists legitimately divide
living things into populations.
3
However, there is a consensus among these experts that the kind
of population that is minimally appropriate for evolutionary biology is the biological population
where biological populations are groups that evolve (descend with modification).
4
Perhaps
Roberta Millstein (2009, 268) puts it best when she says, “Populations are the entities that evolve,
prior to any evolution of species…” Sometimes the members of biological populations interact in
evolutionarily-significant ways, but sometimes they don’t. For instance, Lisa Gannett (2003, 997)
1
The term ‘structure-like’ is Weiss and Long’s (2009, 704). I will define the term in section 4.
2
Notice that I do not question whether the continental groups called ‘populations’ by population
geneticists are really populations. Like Jacob Stegenga (2014, 2), I agree that a population is
whatever a linguistic group calls a ‘population’.
3
For evidence, see Gannett (2003), Millstein (2009, 269), Barker and Velasco (2013), and
Stegenga (2014).
4
For instance, Gannett (2003, 990), Millstein (2009, 268), Stegenga (2014, 2), Barker and Velasco
(2013, 973), Gildenhuys (2014, 437), and Reydon and Scholz (2014, 7) are all interested in
populations understood as groups that evolve.
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has shown that population geneticists sometimes identify populations as breeding units” and
sometimes as “genealogical units.” So, the second question can be restated as, ‘Is the kind of
population being referred to one that requires populations to be groups that evolve?’
The two questions above are philosophically interesting for multiple reasons. Not only is
it interesting to figure out whether continental groups like Native Americans cohere in the right
way to form biological populations, but also, the work done here will inform two ongoing debates
in philosophy of biology. One is about whether there is a single, best conception of a biological
population for evolutionary biology.
5
The other is about whether any of the human continental
groups that population geneticists call ‘populations’ can be legitimately understood as races in any
biological or ordinary sense.
6
My answer to the first question is that the kind of population being referred to in this
context is the K population. This answer constitutes K population theory. While a K population
cannot be understood without jargon from fuzzy set theory and perdurantist metaphysics, it’s
roughly a biological population whose members are united by common genomic ancestry and
whose members can have graded membership. My answer to the second question is that the K
population is indeed a biological population. Note that, in this paper, I do not answer whether
continent-level human genetic clusters are biological populations. That would be an enormous
project that would require defending the sampling scheme, study design, and other methodological
assumptions that go into identifying continent-level human genetic clusters. Rather, what I do is
provide the metaphysical framework for legitimately calling continent-level human genetic
clusters ‘populations’.
I begin by presenting jargon from fuzzy set theory and perdurantist metaphysics that serve
as background assumptions for K population theory. Second, I define the K population. Third, I
defend the K population as the kind of population being referred to when population geneticists
call continent-level human genetic clusters ‘populations’. Fourth, I defend the K population as a
kind of biological population. Fifth, I address potential objections to K population theory. I end
with concluding remarks.
2. Background Jargon
The mathematical and metaphysical jargon that I will use to define the K population is the
following. Suppose crisp sets are the objects called ‘sets’ in Zermelo-Fraenkel set theory. Let an
object space be a crisp set of objects. Suppose a membership function is a function such that
 . Then, a fuzzy set
is a pair 
 
. Unlike crisp sets, has no meaning for fuzzy
sets. The analogous relation is belonging. Suppose
 is s grade of membership (or strength)
in
. Then, belongs to
just in case   

  .
An important point to note is that all of the objects that belong to a fuzzy set need not have
a partial membership in that set. In fact, it could be the case that all of the objects that belong to a
fuzzy set have a strength of 1. In such a case, we say that the fuzzy set’s membership function is
a characteristic function, which is a such that
 . But here are some more useful terms.
5
For contributions to this debate, see Gannett (2003), Millstein (2009; 2015), Barker and Velasco
(2013), and Stegenga (2014), among others.
6
For contributions to this debate, see Glasgow (2003), Kaplan and Winther (2014), Spencer
(2014), and Millstein (2015), among others.
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Suppose the support of
is the crisp set of all objects that belong to
7
Furthermore,
is empty
iff nothing belongs to it.
8
Finally, the cardinality of
is equal to 

.
9
Now suppose we have a crisp set of fuzzy sets Then, a fuzzy set
is the union of all
members of just in case, (a)  , (b) the object space of
is identical to the object space of
each member of , and (c) each
is identical to the maximum value among all strengths of
in each member of .
10
Next, suppose we have a finite, crisp set . Then, a crisp set is a fuzzy
K partition of just in case (i) each member of is a fuzzy set, (ii) each member of is non-
empty, (iii)  , (iv)    , (v) the support of the union of all members of equals
, and (vi) for any object that belongs to any member of , the sum of each grade of membership
that has in each member of is 1. Furthermore, let a member of a fuzzy K partition be a part.
11
Thus, an important point to remember is that a fuzzy K partition is itself a crisp set, but it’s called
‘fuzzy’ because its parts are all fuzzy sets.
Now suppose an object persists iff it exists at different times, an object perdures iff it
persists and it is only partially present at any time it exists, an object that perdures is a perduring
object, and a temporal part is any part of a perduring object that constitutes its partial presence
when it exists.
12
Now I am ready to define the K population.
3. The K Population
Let L be a sexually reproducing species that forms a lineage or a lineage of such species,
but not both.
13
For instance, L could be the species Pan troglodytes (the common chimp) or the
Pan genus (the common chimp and the bonobo).
14
Let be a crisp set of organisms that
constitutes all of the members of L at a time t. Now suppose that any member of has a genome
that consists of a sequence of distinct loci, with one or more alleles at each locus.
15
For instance,
if consists of diploid organisms, then each member of will usually have two alleles at each
locus. Now I will articulate how L can be subdivided into K populations. Let’s start with the
notion of a genomic ancestry partition.
Suppose a crisp genomic ancestry partition of is a fuzzy K partition of such that the
object space for each part in the partition is , each part in the partition is the temporal part of an
7
This definition is needed to articulate condition (v) in the definition of a fuzzy K partition.
8
This definition is needed to articulate condition (ii) in the definition of a fuzzy K partition.
9
This is really the sigma-count cardinality. But I’ll use ‘cardinality’ for convenience. Also, while
this definition is not needed to define a fuzzy K partition, it is needed to articulate the definition
of a K population.
10
This definition is needed to articulate condition (v) in the definition of a fuzzy K partition.
11
All fuzzy-set theoretic definitions are from Zadeh (1965) or Zimmermann (2012).
12
These definitions are from Lewis (1986, 202).
13
The point of defining L in this way is twofold. First, it ensures that members of the same K
population are conspecific. Second, it allows an entire species to be a K population.
14
For the phylogenetic evidence that both the common chimp and Pan form lineages, see Gonder
et al. (2011).
15
By an allele I mean any unique nucleic acid sequence at a locus. Furthermore, this is exactly
how population geneticists talk about alleles in the context of structure-like population structure
analysis. For evidence, see Pritchard et al. (2000) and Tang et al. (2005).
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isolated breeding group, each member’s degree of membership in each part is equal to the
proportion of its genome inherited from an organism in the breeding group that the part represents,
and each part’s membership function is a characteristic function. Also, it will be useful to call
each organism that belongs to a part in a crisp genomic ancestry partition an unmixed organism.
Now, suppose and are distinct crisp sets that constitute all of the members of at
distinct times and , respectively, and that occurs before . Also, suppose is the last time
before when L has a crisp genomic ancestry partition into K parts due to hybridization after .
Thus, hybridization could have begun at or at some time between and . Let be a crisp
genomic ancestry partition of into K parts. Then, a fuzzy genomic ancestry partition of into
K parts, call it , is a fuzzy K partition of such that the object space for each part in is ,
there is a one-to-one correspondence between and , each member’s degree of membership
in each part is equal to the proportion of its genome inherited from an organism that belongs to the
part in that corresponds to the part in under consideration or else an unmixed organism
descended from such an organism, and not every part’s membership function is a characteristic
function. Also, it will be useful to call any organism that belongs to a part in a fuzzy genomic
ancestry partition and has a grade of membership in that part in (0,1) a mixed organism.
For clarity, the hybridization model I am presupposing is the following:
(1) Isolated breeding groups undergo hybridization in such a way that none of the
groups involved become extinct.
(1) is one of three biological possibilities for hybridization between breeding groups. The second
and third are below:
(2) Isolated breeding groups undergo hybridization in such a way that all of the
groups involved become extinct.
(3) Isolated breeding groups undergo hybridization in such a way that some, but
not all, of the groups involved become extinct.
The fact that I am presupposing (1) as the hybridization model for K population theory will be
relevant to how K population theory avoids certain objections. In any case, let a genomic ancestry
partition be a crisp genomic ancestry partition or a fuzzy genomic ancestry partition. Now, I will
define K population parts, which are the temporal parts of K populations.
Suppose we call a part in a genomic ancestry partition a K population part (or KP part for
short). Thus, while KP parts must be indexed to at least one time, they need not exist at exactly
one time. Rather, KP parts can exist through time. Now I’m going to make some distinctions
among KP parts in order to define K populations as, roughly, sequences of KP parts through time.
Suppose
and
are KP parts. Then,
is an offspring of
and
is a parent of
iff
originated from a change in
’s object space or membership function. For instance, suppose at
time we have a fuzzy set of Hadza people that counts as a KP part of humans, call it
’. Then,
at a later time a human dies but not a Hadza person. Also suppose that the latter event produces
a new KP part of humans, call it ‘
’, that has the same support and membership function as
.
Then, it is safe to say that
originated from changing the object space of
, and thus,
is an
offspring of
and
is a parent of
.
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Now, suppose two or more KP parts merge iff their offspring is their union, two or more
KP parts fuse iff they merge and they’re approximately equal in cardinality, and two or more KP
parts join iff they merge but do not fuse. Furthermore, let a parent with much lower cardinality in
a joining be a minor parent. Then, a KP beginning is any KP part that originated from its parent(s)
through hybridization, budding, splitting, or fusion. In contrast, a KP end is any KP part that has
no offspring after multiple generation times for its species, has offspring that arose from splitting,
has offspring that arose from fusion, or is a minor parent and has offspring that arose from joining.
We are finally in a position to define a K population.
(4) A K population is, essentially, a perduring object such that all of its temporal
parts are unique KP parts, its successive temporal parts are related as parent and
offspring, its first temporal part is a KP beginning, and it perdures until one of
its temporal parts is a KP end.
So, K populations are, roughly, perduring objects whose temporal parts are fuzzy sets of
conspecific organisms that cohere via common genomic ancestry. Here are some final terms that
will be useful for talking about K populations. First, an organism is a member of a K population
at a time iff it belongs to that K population’s temporal part at . Second, an organism’s grade of
membership in a K population at a time is equal to its grade of membership in that K population’s
temporal part at . Now that I have introduced what a K population is, we can turn to answering
whether population geneticists are actually referring to K populations when they call continent-
level human genetic clusters ‘populations’.
4. The Evidence for K Population Theory
There is plenty of evidence that population geneticists are referring to the K population
when they call continent-level human genetic clusters ‘populations’. But first, here’s some
clarification. The linguistic context in which continental population talk is used in population
genetics is population structure analysis that utilizes structure-like clustering algorithms.
Structure-like clustering algorithms (e.g. structure, frappe, admixture, etc.) attempt to infer
population structure in a group of organisms using an algorithm that searches for the fuzzy K
partition that maximizes allele frequency differences among parts using genotype data from the
organisms. Sometimes what’s investigated is the optimal partition into K parts, and other times
the task is to find the optimal partition at each K level. Suppose we call any population structure
analysis that uses a structure-like clustering algorithm ‘structure-like PSA’. Then, two examples
of structure-like PSA are Gonder et al.’s (2011) use of structure to identify the population structure
of chimpanzees, and Li et al.’s (2008) use of frappe to identify the population structure of humans.
If the continent-level human genetic clusters inferred in structure-like PSA are K
populations, then we can explain how population geneticists model, sample, talk, and otherwise
behave in structure-like PSA. With respect to modeling, the representations in the models used in
structure-like algorithms are what we would expect if the populations being inferred are K
populations.
For example, one important model assumption of structure-like algorithms is that
populations leave behind a genetic trail. This is because structure-like algorithms represent
populations as sequences of allele frequencies (Pritchard et al. 2000, 947; Tang et al. 2005, 290).
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However, K population theory predicts this model assumption nicely because the fact that K
populations leave behind a genetic trail in the form of a sequence of allele frequencies can be
derived from what a KP part is.
Second, K population theory explains why population geneticists sample in the way that
they do. For example, the widespread practice in fuzzy PSA of sampling ancestry informative
markers” for cluster analysis suggests that what is being sought are genealogical groups, which K
populations are by definition (Tishkoff and Kidd 2004, S23, S25). In fact, one population
geneticist, Marcus Feldman (2010, 157), coined the term ‘ancestry group’ to talk about continent-
level human genetic clusters.
Third, K population theory predicts how population geneticists talk in fuzzy PSA. For
example, population geneticists have no problem saying that continent-level human genetic
clusters originated thousands of years ago, which would make little sense unless the clusters were
perduring objects, like K populations.
16
Finally, K population theory explains many other
behaviors of population geneticists in structure-like PSA that would otherwise be peculiar. For
one, K population theory explains why population geneticists check the accuracy of structure-like
algorithms in the way that they do. For example, Shringarpure and Xing (2014) tested the accuracy
of admixture’s population membership grade assignments by judging how well it predicted “the
proportion of YRI ancestry” in individuals of a fabricated admixed population.
17
Nevertheless, one objection is that K population theory is pointless because the actual
population concept being used here is inconsistent. For example, some population geneticists,
such as Feldman and Lewontin (2008, 81, 90), have no problem calling the continental clusters
both “geographical populations” and ancestry groups’, while others, such as Weiss and Long
(2009, 709), are fine calling the clusters “geographic populations,” but find the ‘ancestry group’
label misleading.
While it is true that there is no consensus among population geneticists about how to define
population when using it to talk about continent-level human genetic clusters in structure-like
PSA, K population theory is not a project in conceptual analysis, it’s a theory about what
‘population’ refers to in a particular context. This project is compatible with there being
inconsistent beliefs about what ‘population’ means as long as we adopt a referential semantics for
‘population’. Yet another concern is that even if K population theory is right, it still remains to be
shown that the K population is a biological population. So to that I now turn.
5. Why K Populations are Biological Populations
Remember that biological populations must be, at the very least, groups that evolve. Thus,
if K populations are not, at least, groups that evolve, then the population geneticists who are
acknowledging continental populations in humans are using the word ‘population’ in a way that is
not helpful for evolutionary research. However, K populations are groups that evolve, and this
fact can be derived from the definition of a K population.
Suppose we have an arbitrary K population p and we want to know how it originated. By
definition, the first temporal part of p is a KP beginning. Furthermore, KP beginnings, by
definition, are offspring from one or more KP parts that are distinct from it. But for a KP part to
16
For an example, see Wang et al. (2007, 2060).
17
‘YRI’ is shorthand for ‘Yoruba Nigerians from Ibadan’.
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be an offspring it must be a modified version of its parent, either in its object space or membership
function. Thus, p will be a modified descendant the moment its first temporal part originates.
Nevertheless, it’s an empirical question whether p originated from another K population since the
KP part that p originated from could be a temporal part of a different kind of biological
population.
18
In any case, it’s easy to see how K populations form evolutionary networks.
6. Objections and Replies
There are a few objections that one could have with K population theory. First, in a recent
paper, Millstein (2015, 9-10) has offered some healthy skepticism about interpreting continent-
level human genetic clusters as biological populations. Millstein rightly points out that another
possible interpretation is that continent-level human genetic clusters are not populations, but are
just ancestry groups (in Feldman’s sense) of organisms descended from members of past
populations.
While Millstein’s suggestion is a plausible interpretation of some inferred clusters in
structure-like PSA, it’s not a plausible interpretation of continent-level human genetic clusters.
This is because the hybridization model that Millstein is using, which is (2), is not the model that
best fits how population geneticists talk about continental clusters. In order for Millstein’s model
to be correct, population geneticists would have to think that continent-level human genetic
clusters represent only past non-random mating structure. However, population geneticists
regularly talk about continent-level human genetic clusters as reflecting current non-random
mating structure. An example is below.
Each of the large population groups (the Sub-Saharan African farmers, Eurasia, and
East Asia) can be considered as a metapopulation consisting of populations with
some genetic exchange between them and with a common ancestry (Zhivotovsky
et al. 2003, 1183).
19
These authors do not sound like they’re just talking about past non-random mating structure. Of
course, this way of talking is easily explained if we accept that population geneticists intend
continental clusters to represent extant breeding groups. Furthermore, if that is true, then the
appropriate hybridization model is (1), not (2), which is the model used in K population theory.
For clarity, I am not saying that population geneticists are correct in using (1) to model
hybridization among continental groups of humans. All I am saying is that the hybridization model
that they are in fact using when they call continent-level human genetic clusters ‘populations’ is
(1). Second, both Millstein (2009, 269) and Peter Gildenhuys (2014, 434-437) have claimed
that a respectable notion of biological population must be able to explain how populations are
modeled in evolutionary dynamics. For instance, Millstein (2009, 269) claims that migration
modeling in population genetics (e.g. the source-sink model, the island model, etc.) requires that
migration amounts to organisms wholly leaving one population and wholly joining another
18
This quirk is intentional since it allows K populations to have evolved from a more primitive
kind of biological population.
19
See Zhivotovsky et al. (2003, 1174) for what they mean by “farmers.”
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population. However, since organisms are born into K populations in virtue of their genomic
ancestry, it’s hard to imagine how the members of K populations can migrate in the sense given
above.
This is a good concern. The members of K populations are indeed born into them. And
barring the extinction of one’s K population or one’s species, it’s impossible for a member of a K
population to leave her population. However, the latter is a concern only if one presupposes
monism” about classifying individuals into biological populations, as Millstein (2015, 6) does.
20
However, if one does not presuppose monism, but rather, accepts the possibility of “population
pluralism”—which Stegenga (2014, 1) defines as the view that “there are many ways that a
particular grouping of individuals can be related such that the grouping satisfies the conditions
necessary for those individuals to evolve together”—then the fact that the members of K
populations don’t migrate is not a flaw.
21
Rather, it’s just an example of how classifying organisms
into K populations is not going to be useful in all research projects in population genetics.
In fact, Mishler and Brandon (1987, 401) would call K populations historical entities
because they’re essentially genealogical groups (e.g. monophyletic groups). Furthermore, because
they’re historical entities, they’re useful in history-oriented population-genetic research, such as
research on “the history of human migrations” and “human evolutionary history” (Rosenberg et
al. 2002, 2384; Rosenberg et al. 2005, 661).
22
7. Conclusion
In this paper, I have attempted to show that there is a legitimate metaphysical basis for
calling continent-level human genetic clusters ‘populations’. Namely, these groups could be
biological populations in virtue of being K populations. I defended my position by, first,
articulating a K population as, roughly, a biological population whose members are united by
genomic ancestry and population membership is graded. However, the precise definition was
given in (4). Next, I provided evidence that population geneticists are referring to the K population
in population structure analyses that use structure-like clustering algorithms. After this, I defended
K populations as authentic biological populations. Finally, I defended K population theory against
two objections.
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It’s worth noting that Millstein (2015, 6) considers her monism a “defeasible monism,” since
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21
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Tishkoff, S., & Kidd, K. (2004). Implications of Biogeography of Human Populations for 'Race'
and Medicine. Nature Genetics, 36(11), S21-S27.
Wang, S., Lewis, C., Jakobsson, M., Ramachandran, S., Ray, N., Bedoya, G., . . . Rothhamm.
(2007). Genetic Variation and Population Structure in Native Americans. PLoS Genetics,
3(11), e185: 2049-2067.
Weiss, K., & Long, J. (2009). Non-Darwinian Estimation: My ancestors, my genes' ancestors.
Genome Research, 19, 703-710.
Zadeh, L. (1965). Fuzzy Sets. Information and Control, 8, 338-353.
Zhivotovsky, L., Rosenberg, N., & Feldman, M. (2003). Features of Evolution and Expansion of
Modern Humans, Inferred from Genomewide Microsatellite Markers. American Journal
of Human Genetics, 72, 1171-1186.
Zimmermann, H. (2012). Fuzzy Set Theory and Its Applications: Fourth Edition. New Delhi:
Springer.
... Different population concepts may be appropriate in other contexts. For an example, see Spencer (2016). For a defense of population pluralism, see Stegenga (2016 ...
Article
An aim of science is to increase our understanding of the natural world. A primary means for doing so is by providing explanations, which often proceed by tracing the causes of phenomena. How can a causal explanation lead to understanding? While explanations can take many forms, I argue that to succeed they must embody a conception of causation shared with their audience. The challenge then, is to describe this conception and detail its role in explanation. While there is good evidence that scientists employ more than one causal concept, I argue that the concept of productive causation (centered on the notion of bringing about change via a connection) has a primary role in natural science explanations. After critiquing other philosophical accounts, I develop a new theory of productive causation and show how it provides an underpinning for successful explanations. The heart of the theory is a network of persisting processes that possess dispositions toward change-producing mutual interactions. I argue that in a good explanation, the scientific entities, properties and activities invoked will correspond to the theory’s depiction of causal structure. One important dimension of the theory describes how repeated patterns of interaction can give rise to a hierarchy of composite processes. This allows the theory to account for stabilized entities at various spatio-temporal scales. In turn, this enables the approach to be applicable throughout the natural sciences. After starting with simple examples, I show how the theory deals with more challenging cases from physics to biology. I conclude that the approach illuminates how explanations of various forms across diverse disciplines can lead to scientific understanding.
... humans, but what it tells you will depend on the type of genetic data and statistical methods you happen to use, as well as on the number of terminal branches into which you sort your population (Gannett 2004;Spencer 2016). ...
Article
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Since 2013, an organization called the Nonhuman Rights Project has brought before the New York State courts an unusual request—asking for habeas corpus hearings to determine whether Kiko and Tommy, two captive chimpanzees, should be considered legal persons with the fundamental right to bodily liberty. While the courts have agreed that chimpanzees share emotional, behavioural, and cognitive similarities with humans, they have denied that chimpanzees are persons on superficial and sometimes conflicting grounds. Consequently, Kiko and Tommy remain confined as legal "things" with no rights. The major moral and legal question remains unanswered: are chimpanzees mere "things", as the law currently sees them, or can they be "persons" possessing fundamental rights? In Chimpanzee Rights: The Philosophers’ Brief, a group of renowned philosophers considers these questions. Carefully and clearly, they examine the four lines of reasoning the courts have used to deny chimpanzee personhood: species, contract, community, and capacities. None of these, they argue, merits disqualifying chimpanzees from personhood. The authors conclude that when judges face the choice between seeing Kiko and Tommy as things and seeing them as persons—the only options under current law—they should conclude that Kiko and Tommy are persons who should therefore be protected from unlawful confinement "in keeping with the best philosophical standards of rational judgment and ethical standards of justice." Chimpanzee Rights: The Philosophers’ Brief—an extended version of the amicus brief submitted to the New York Court of Appeals in Kiko’s and Tommy’s cases—goes to the heart of fundamental issues concerning animal rights, personhood, and the question of human and nonhuman nature. It is essential reading for anyone interested in these issues.
... humans, but what it tells you will depend on the type of genetic data and statistical methods you happen to use, as well as on the number of terminal branches into which you sort your population (Gannett 2004;Spencer 2016). ...
Book
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In December 2013, the Nonhuman Rights Project (NhRP) filed a petition for a common law writ of habeas corpus in the New York State Supreme Court on behalf of Tommy, a chimpanzee living alone in a cage in a shed in rural New York. Under animal welfare laws, Tommy’s owners, the Laverys, were doing nothing illegal by keeping him in those conditions. Nonetheless, the NhRP argued that given the cognitive, social, and emotional capacities of chimpanzees, Tommy’s confinement constituted a profound wrong that demanded remedy by the courts. Soon thereafter, the NhRP filed habeas corpus petitions on behalf of Kiko, another chimpanzee housed alone in Niagara Falls, and Hercules and Leo, two chimpanzees held in research facilities at Stony Brook University. Thus began the legal struggle to move these chimpanzees from captivity to a sanctuary, an effort that has led the NhRP to argue in multiple courts before multiple judges. The central point of contention has been whether Tommy, Kiko, Hercules, and Leo have legal rights. To date, no judge has been willing to issue a writ of habeas corpus on their behalf. Such a ruling would mean that these chimpanzees have rights that confinement might violate. Instead, the judges have argued that chimpanzees cannot be bearers of legal rights because they are not, and cannot be persons. In this book we argue that chimpanzees are persons because they are autonomous.
... This is a pretty common interpretation of what population geneticists mean by 'biological population' according to philosophers of biology. For evidence, seeMillstein (2009), Stegenga (2016,and Spencer (2016). ...
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In the early 2000s, Esteban Burchard and his colleagues defended a controversial route to the view that there’s a racial classification of people that’s (epistemically) useful in medicine. The route, which I call ‘Burchard’s route,’ is arguing that there’s a racial classification of people that’s useful in medicine because, roughly, there’s a racial classification with medically relevant genetic differentiation (Risch et al. in Genome Biol 1–12, 2002; Burchard et al. in N Engl J Med 348(12):1170–1175, 2003). While almost all scholars engaged in this debate agree that there’s a racial classification of people that’s useful in medicine in some way, there’s tremendous controversy over whether any racial scheme is useful in medicine because there are medically relevant genetic differences among those races (Yudell et al. in Science 351(6273): 564–565, 2016). The goal of this paper will be to show that Burchard’s route is basically correct. However, I will use a slightly different argument than Burchard et al.’s in order to provide a firmer foundation for the thesis, both metaphysically and genetically. I begin by reviewing Burchard’s route and its critics. Second, I present an original argument for establishing Burchard et al.’s conclusion using a Burchard-like route. I call it ‘Spencer’s route’. I reply to major objections along the way, and I end with a summary.
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In this chapter, we address the ethical challenges raised by chimera research policy, using as a case study the National Institutes of Health (NIH) 2016 proposal to change its policy governing the funding of human–nonhuman animal chimera research. In this case, we find a troubling shift from a focus on nonhuman animal welfare to poorly thought-out concerns with humanization. Despite the restrictions on modifying early-stage nonhuman primates, the proposed changes make it possible to modify animals in ways that may significantly impact neurological functions and behavioral capacities with serious implications for the welfare of research subjects. The NIH’s restrictions target the development of humanized brains—particularly in nonhuman primates—reflecting, we suspect, a concern to avoid creating chimeras that are in some sense “too human.” While we endorse robust restrictions on chimera research, particularly in the face of a growing globalization of research in varied and inconsistent regulatory environments, we maintain that policies should not be based on beliefs about inherent human uniqueness but should (minimally) instead conform to the widely accepted 3Rs framework for research involving nonhuman animals, and our best welfare science.
Chapter
Social media is awash with the latest discoveries about the human past. Headlines read: “DNA of ancient skeleton linked to modern indigenous peoples,” “Ancient DNA suggests the first Americans sidestepped the glaciers,” and “Ancient DNA reveals secrets of human history.” These headlines all come from respected outlets with a connection to the academic community (Smithsonian, Science, and Nature, respectively). However, news media outlets with a more popular audience have also become interested in the stories and histories being revealed about our ancestors through modern and ancient DNA, and new toolkits include a heavy bioinformatics component. As expected, these headlines are a bit more sensational: “Are you tall? Then thank your ancient cousins: Neanderthal DNA still helps dictate your HEIGHT and whether you suffer from lupus and schizophrenia” reads a Daily Mail headline about Neanderthal admixture. Another reads: “The ‘founding father’ of Europe: DNA reveals all Europeans are related to a group that lived around 35,000 years ago.” Yet another, seemingly contradictory, headline reads: “Europeans drawn from three ancient ‘tribes.’” Communications technology has evolved in lockstep with advances in DNA recovery and analysis over the last two decades, perhaps giving the impression that studies of past population movements are a recent development. However, interest in “ancient migrations” has a deep history in western science, beginning with analyses of the skull using what are now referred to as biological distance methods.
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While evolutionary thinking is increasingly becoming popular in fields of investigation outside the biological sciences, it remains unclear how helpful it is there and whether it actually yields good explanations of the phenomena under study. Here we examine the ontology of a recent approach to applying evolutionary thinking outside biology, the generalized Darwinism (GD) approach proposed by Geoffrey Hodgson and Thorbjørn Knudsen. We examine the ontology of populations in biology and in GD, and argue that biological evolutionary theory sets ontological criteria that GD fails to meet. We suggest two options to revise the population concept in GD: reformulating the concept in terms of inheritance and reproduction such that it comes to pick out individuals similar to evolving populations, or trying to build an adequate population concept on a principle of differential retention instead of differential reproduction. 1 Introduction 2 Generalized Darwinism 2.1 What is generalized Darwinism? 2.2 Darwinian principles 3 The Ontology of Generalized Darwinism: What Are Populations? 3.1 The population concept of generalized Darwinism 3.2 The population concept in evolutionary theory 4 Locating Evolving Systems in Generalized Darwinism 5 Conclusion </l
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We reject a widespread objectivism about kinds of evolutionary groups in favor of a new conventionalism. Surprisingly, being any one kind of evolutionary group typically depends on which of many incompatible values are taken by suppressed variables. This novel pluralism underlies almost any single evolutionary group concept, unlike familiar pluralisms claiming that multiple concepts of certain sorts are legitimate. Consequently, we must help objective facts determine which candidate evolutionary groups satisfy the definition of a given evolutionary group concept, regardless of whether we also help determine the legitimacy of that concept’s applications.
Article
We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
Article
It has become customary among philosophers and biologists to claim that folk racial classification has no biological basis. This paper attempts to debunk that view. In this paper I show that ‘race’, as used in current US race talk, picks out a biologically real entity. I do this byfirst showing that ‘race’, in this use, is not a kind term, but a proper name for a set of human population groups. Next, using recent human genetic clustering results, I show that this set of human population groups is a partition of human populations that I call ‘the Blumenbach partition’. © 2014 by the Philosophy of Science Association. All rights reserved.
Article
I criticize some arguments against the causal interpretability of population genetics put forward by Denis Walsh ([2007], [2010]). In particular, I seek to undermine the contention that population genetics exhibits frame of reference relativity or subjectivity with respect to its formal representations. I also show that classical population genetics does not fall foul of some criteria for causal representation put forward by James Woodward ([2003]), although those criteria do undermine some causalist stances. • 1 Introduction • 2 Modularity • 3 The Crucially Important Point • 4 The Gillespie Case: Density-Dependent Selection • 5 Conclusion
Article
I defend a radical interpretation of biological populations—what I call population pluralism—which holds that there are many ways that a particular grouping of individuals can be related such that the grouping satisfies the conditions necessary for those individuals to evolve together. More constraining accounts of biological populations face empirical counter-examples and conceptual difficulties. One of the most intuitive and frequently employed conditions, causal connectivity—itself beset with numerous difficulties—is best construed by considering the relevant causal relations as ‘thick’ causal concepts. I argue that the fine-grained causal relations that could constitute membership in a biological population are huge in number and many are manifested by degree, and thus we can construe population membership as being defined by massively multidimensional constructs, the differences between which are largely arbitrary. I end by showing that positions in two recent debates in theoretical biology depend on a view of biological populations at odds with the pluralism defended here. • 1 Introduction • 2 Biological Population, Broad and Narrow • 3 Difficulties with Narrow Biological Population Conditions • 3.1 Against the genealogical condition • 3.2 Against the conspecificity condition • 3.3 Against the proximity condition • 3.4 Against the typology condition • 4 Causal Connectivity • 5 Massively Multidimensional Population Constructs • 6 Population Uniqueness and Natural Selection • 6.1 Statisticalism and its discontents • 6.2 Price at what price? • 7 Conclusion
Biologists and philosophers have offered differing concepts of biological race. That is, they have offered different candidates for what a biological correlate of race might be; for example, races might be subspecies, clades, lineages, ecotypes, or genetic clusters. One thing that is striking about each of these proposals is that they all depend on a concept of population. Indeed, some authors have explicitly characterized races in terms of populations. However, including the concept of population into concepts of race raises three puzzles, all having to do with time. In this paper, I extend the causal interactionist population concept (CIPC) by introducing some simple assumptions about how to understand populations through time. These assumptions help to shed light on the three puzzles, and in the process show that if we want to understand races in terms of populations, we will need to revise our concept(s) of race. Copyright © 2015 Elsevier Ltd. All rights reserved.