## 1. What is Biodiversity?



Strangely enough, we can roughly date when the term
'biodiversity' was coined (Takacs 1996: 34-40). It
occurred during the National Forum on BioDiversity sponsored by the
Smithsonian Institution and the National Academy of the Sciences
21-24 September 1986. Approximately 14,000 people attended what was a
media spectacle that included the likes of Jared Diamond, Paul
Ehrlich, Thomas Eisner, G. Evelyn Hutchinson, Ernst Mayr, Charles D.
Michener, Harold A. Mooney, Peter Raven, and E. O. Wilson. The coiner
of the term is thought to be Walter G. Rosen. He said,



> 
> 
> It was easy to do: all you do is take the 'logical' out of
> 'biological'.... To the take the logical out of
> something that's supposed to be science is a bit of a
> contradiction in terms, right? And yet, of course, maybe so logical
> that there seems to be no room for emotion in there, no room for
> spirit. (Takacs 1996: 37)
> 
> 
> 



As David Takacs noted, a search in *Biological Abstracts* for
the year 1988 would not contain it as a keyword. For 1993, it appeared
seventy-two times. As of March 2015, Google Scholar reports that
'biodiversity' appears in the title of over one hundred
thousand papers. The term clearly has caught on. But, we are getting
ahead of ourselves.



Conservation biology clearly concerns conserving something biological
or ecological, but what is or should be conserved? Work has focused on
a variety of units. As examples, some have focused on species such as
the spotted owl *Strix occidentalis* (Yaffee 1994) and
loggerhead turtle *Caretta caretta* (Bolten & Witherington
2003); some have focused on populations and sub-species such as wild
salmon *Oncorhynchus spp.* (Quinn 2005); some have focused on
biomes or eco-regions such as tropical coral in the Great Barrier Reef
(Hutchings, Kingsford, & Hoegh-Guldberg 2008); and finally some
have focused on genotypes or genetic features such as heterozygosity
(Avise & Hamrick 1996). However, in the 1980s, conservation
biologists united and argued that *biodiversity* should be the
focus of the discipline (E. O. Wilson 1988; see entry on
 biodiversity).
 What then is biodiversity? Here is a standard definition from an
influential textbook.



> 
> 
> Biological diversity, or biodiversity, is the sum total of all living
> things--the immense richness and variation of the living world.
> Biodiversity can be considered at many levels of biological variation,
> ranging from genetic variability within a species, to the biota of
> some selected region of the globe, to the number of evolutionary
> lineages and the degree of distinctness among them, to the diversity
> of ecosystems and biomes on Earth. (Groom et al. 2006: 27)
> 
> 
> 



Informally then, biodiversity is variation at all levels of the
biological hierarchy. To conserve biodiversity would then to be to
conserve all of this variation. However, there are two fundamental
issues to be resolved. First, there is an issue of practicality. As
philosopher and conservation biologist Sahotra Sarkar writes,



> 
> 
> Conserving biodiversity, and construing the term intuitively to refer
> to all the biological diversity that there is, at every level of both
> hierarchies amounts to saying that "biodiversity" refers
> to all biological entities. "Biodiversity" in effect
> becomes all of biology. Conservation would be an impractical proposal
> if biodiversity were construed in this way: everything biological
> would become a goal of conservation. (Sarkar 2005: 180)
> 
> 
> 



In fact the issue is more complicated than even this. In order to
estimate the amount of biodiversity with regard to a place, we must
find some true surrogate (Margules & Sarkar 2007: Ch. 2). A true
surrogate is some feature that strongly correlates with biodiversity.
Suppose we used species as a true surrogate. With few exceptions, we
cannot simply observe the species present in some locale; we can only
observe some of its members. We must use some estimator surrogate for
the true surrogate too; e.g., soil, vegetation, precipitation, etc.
Thus, we estimate biodiversity twice removed. Second, since we cannot
conserve all biological variation, we need a principled way to decide
what to conserve. As we saw above, conservation biologists have
refined their account to include variation in genes, species, and
ecosystems. This is still far too broad since there is a great deal of
"junk DNA"; often we want to conserve wild populations
(e.g., wild salmon versus hatchery salmon); and rare habitats are
often considered more important than common ones (Morgan 2009).



In the remainder of this section, we consider three recent proposals
concerning what biodiversity is (and is not) that try to characterize
biodiversity in a practical and principled manner.


### 1.1 Biodiversity as Multidimensional



One attempt to provide a principled and practical way to characterize
biodiversity comes from Maclaurin & Sterelny (2008). James
Maclaurin and Kim Sterelny start with the suggestion that we find the
relevant "natural" units and then provide means for
measuring variation with regard to those units. By
'natural' units, they mean ones that identify properties
that are recognizable and are causally salient (Maclaurin &
Sterelny 2008: 10). As discussions of phenetic taxonomy have shown,
measures of "overall similarity" are fraught with
subjectivity. This subjectivity depends on a choice of distance metric
and on how one articulates character traits (Ereshefsky 2000:
60-66). Contemporary taxonomists group taxa according to
patterns of evolutionary descent, which is a method called
"cladistics", precisely to avoid such subjectivity given
that the evolutionary history of life is natural in the above sense
(Baum & Smith 2013).



Maclaurin and Sterelny contend that the fundamental unit of
biodiversity is species and specifically species richness.
Notoriously, biologists and philosophers have debated the nature of
species. However, Maclaurin and Sterelny defend an evolutionary
concept of species which states that "Evolutionary species are
lineages of organisms with their 'own evolutionary tendencies
and historical fate'" (Maclaurin & Sterelny 2008: 33;
see Wiley 1978). They suggest that this is the more natural approach
to species because,



> 
> 
> The living world is organized into phenomenological species:
> recognizable, reidentifiable clusters of organisms. This fact makes
> the production of bird and butterfly field guides, identification keys
> for invertebrates, regional floras, and the like, all
> possible.... There are many routes through which one population
> can become demographically isolated from, and hence evolutionarily
> independent of, populations that were once sources and sinks of its
> own genes. But the fact of isolation and evolutionary independence is
> of immense importance to the fate of local adaptation in such
> populations. So the phenomenological species richness of a region is,
> in an importance sense, a catalogue both of phenotypic variety and of
> the potential evolutionary resources available in that region.
> (Maclaurin & Sterelny 2008: 40)
> 
> 
> 



That is, though a variety of evolutionary processes lead to the
formation and cohesion of organisms into species, there nevertheless
is a uniformity of pattern in the natural world.



Species richness with regard to a given community, a group of
interacting species in a given place, is the number of species
present. We might also add species evenness to species richness
(Magurran 1988, 2004). With respect to a given community, there will
be some number of species. However, suppose we have two species in
which the first has 300 members but the second has 700. Suppose again
we have two species where the first has 500 members and the second 500
as well. The second community of species is far more
"even" than the first. One formal representation of
evenness is as follows (Magurran 1988). We start with the
Shannon-Weiner diversity index.

 

\[
H' = -\sum\_{i=1}^{S} p\_i \ln p\_i
\]


Here \(p\_i\) is the proportion of organisms belonging to the \(i\)th
species and \(S\) is the total number of species. If we define the
following,

 

\[
H\_\text{max}' = - \sum\_{i=1}^{S}\frac{1}{S} \ln \frac{1}{S} = \ln S
\]


We now have a measure of species evenness,

 

\[
J' = \frac{H'}{H\_\text{max}'}
\]


\(J'\) takes a value between 0 and 1 and the closer to the value of 1,
the less variation between the number of individuals in a species.



The second way in which we might augment species richness is through
disparity, morphological diversity, or more simply, phenotypic
variation (Maclaurin & Sterelny 2008: Ch. 3, 4; see Gould 1989 for
a discussion of disparity in the history of life). Disparity,
morphological diversity, or phenotypic vacation in general, concern
the variation in phenotypic traits exhibited in and between species.
Suppose we have one suite of species that are very similar
phenotypically; however, we might have another suite of species that
vary a great deal phenotypically. A measure of such variation would
weight species richness with some parameter(s) representing the
variation in phenotypic traits. Unfortunately, if we include such
phenotypic variation *per se* we find ourselves exactly with
the problems that dogged phenetic taxonomy and the attempt to capture
some notion of overall similarity. Additionally, if we focus on
disparity *per se* as opposed to mere phenotypic variation, we
must try to capture some notion of basic "body plan"
(Arthur 2000).



Maclaurin and Sternly suggest that one way to operationalize such
variation is through the notion of a *morphospace* (Maclaurin
& Sterelny 2008: Section 4.4). Suppose we have a \(n\)-dimensional
hyperspace where each dimension \(i\) is a variable concerning the
\(i\)th phenotype. Thus, a point in the space represents a specific
organism and their phenotype trait. Associated with any given species
would be a hypervolume representing the phenotypic variation of that
species. Though there are not many worked out hyperspaces of this
sort, a famous one is paleontologist David Raup's analysis of
the coiling patterns in mollusks that includes variables for whorl
expansion rate, uncoiling rate, translation of the generating curve,
and shape of the growing edge (Raup 1966).



![A line drawing of a cube with 14 different pictures of mollusk shells around it, lines connect each picture to a particular part of the cube. Axes are labeled 'Expansion rate (w)' , 'Translation (t)', and 'Distance (d) of Generation Curve from Axis' ](Raup.png)

Figure 1: Raup's coiling patterns
in mollusks morphospace (Raup 1966)




The morphospace describes actual coiling patterns and merely possible
ones, and raises *explananda* for evolutionary biologists. Why
did certain coiling patterns evolve and not others? Maclaurin and
Sterelny claim morphospaces will be of low dimensionality and
specifically limited to lineages or clades (Maclaurin & Sterelny
2008: 75-9). Thus, biologists must be assured that the variables
included are representative of the phenotypic traits of the organisms
and hence species under study. They hypothesize that the variables
will correspond to developmental modules or mosaics, which are a
relatively independent developmental system that contributes to
fitness (G. P. Wagner 2014; P. J. Wagner 1995). This approach of
course assumes that there exist independent developmental systems, and
ideally it would help articulate how a lineage would fare over time as
a function of its plasticity.



Another component of biodiversity according to Maclaurin and Sterelny
concerns the ecological interactions between species (Morin 2011).
This additional structure is given by interspecific competition,
predator-prey relations, and mutualism along with possible
higher-order ones like trophic cascades. In the absence of such
interactions, a group of species is nothing more than the "sum
of the parts" (Clements 1916; Eliot 2011; Gleason 1926;
Odenbaugh 2007; R.H. Whittaker 1967). In the presence of such
interactions, they hypothesize the existence of emergent properties
that are not a "simple reflection" of the species
themselves (Maclaurin & Sterelny 2008: 120). Additionally, even if
there were no such emergent properties, we would like to know what
their causal contribution is: how to do they drive community patterns?
Infamously, some ecologists have hypothesized that as the diversity of
a community increases, so does the stability of the community (Egerton
1973; Elton 2000; MacArthur 1955; May 1973/2001; Tilman, Wedin, &
Knops 1996). 'Diversity' can be understood as the number
of species present, the number of interactions, or intensity of those
interactions. 'Stability' can be understood in a variety
of ways from notions of Lyapunov stability, return time after a
perturbation, biomass stability, etc. (Justus 2008; Pimm 1984).
Ecologists have theorized, experimented, and collected a great deal of
data regarding this hypothesis, but its truth remains elusive.
Maclaurin and Sterelny suggest we can articulate an ecospace including
at least the boundedness, degree of emergent properties, and internal
regulation as variables to describe ecological communities (Maclaurin
& Sterelny 2008: Ch. 6). One serious worry is whether any of these
features can be measured in an informative way. But, insofar as there
are no community boundaries, no emergent properties, and no internal
regulation, there are no communities. The robustness of communities
would require each of these take non-zero values. Additionally, these
variables are not independent and hence the ecospace would have to
take account of the interactions between each variable. For example,
internal regulation might be a function of the emergent properties
possessed by the community, and the boundaries of a given community
determined by those very properties (Sterelny 2006).



Finally, we can consider how to include phylogenetic information in
our measure of biodiversity. One method comes from Daniel Faith (1992,
1994, 2002). In cladistics, we use character traits to divide
organisms into groups. We begin with the traits shared by all of the
organisms and then articulate smaller groups by traits unique to them
where each then is a clade. A cladogram then displays the distinct
character traits of each group. Cladograms differ from phylogenetic
trees insofar as the latter includes ancestor-descendant relations
apart from merely relations of nested similarity. For any phylogenetic
tree, there are many cladograms consistent with it. Faith writes,



> 
> 
> The phylogenetic diversity ('PD') of \(s\) is equal to the
> sum of the lengths of all those branches that are members of the
> corresponding minimum spanning path. (Faith 1992: 4)
> 
> 
> 



Consider the following cladogram.



![a diagram with a diagonal line going from bottom left to upper right, labeled at the end with '2'. Along the line are 12 evenly placed tic marks. From this line and going to the left are 6 perpendicular lines, each also with evenly placed tic marks. The first, before the first tic mark on the main line, has 4 tic marks and is labeled '10'; between its second and third tic mars is another perpendicular line going to the right with one tic mark. The second, between the third and fourth tic marks on the main line, has only one tic mark. The third, between the fourth and fifth tic marks, has 7 tic marks and is labeled '8'. The fourth, between the fifth and sixth tic marks of the main line, has 5 tic marks and is labeled '6'. On it are two perpendicular lines with one tic mark each going to the right between the second and third and third and fourth tic marks. The fifth line, between the seventh and eighth tic marks on the main line, has 3 tic marks. The last line, between the eighth and ninth tic marks on the main line, has 1 tic mark. ](Faith.jpeg)

Figure 2: The phylogenetic diversity
represented in a cladogram (Faith 1992: 3)




In Figure 2, we have taxa 2, 6, 8, 10. The minimum spanning path
connecting each requires 28 character changes and thus its PD is 28.
As Maclaurin and Sterelny note, this assumes that we have an adequate
sample of character traits and given that many are molecular they do
not tell us much about phenotypes (Maclaurin & Sterelny 2008:
141). Thus, they suggest we stick with local morphospaces.



Maclaurin and Sterelny's approach is very suggestive but as we
have seen its execution by biologists requires making good on using
species richness augmented with information regarding phenotypic
variation, ecology, development, and phylogeny into an empirical
tractable hyperspace. The extent to which this can be done is unclear
(Justus 2010; Morgan 2009). Their approach also presupposes a
particular account of species as well.


### 1.2 Operationalizing Biodiversity



Sahotra Sarkar attempts to define biodiversity
"implicitly" as opposed to "explicitly"
contrary to Maclaurin and Sterelny (Margules & Sarkar 2007; Sarkar
2002, 2005; Sarkar & Margules 2002). A concept is given an
explicit definition if, and only if, there is a set of necessary and
sufficient conditions for the satisfaction of the concept. For
example, suppose we define the concept species
phylogenetically in the following way: necessarily, something is a
species if, and only if, it is a lineage between two speciation events
or a speciation event and an extinction event (Ridley 1989). An
implicit definition is different; the concept is defined by some rules
or axioms in which the concept appears. So, Sarkar characterizes biodiversity
as follows,



> 
> 
> [B]iodiversity should be (implicitly) operationally defined as what is
> being optimized by the place prioritization procedures that prioritize
> all places on the basis of their biodiversity content using true
> surrogates. (Sarkar 2005: 182)
> 
> 
> 



To understand this account, we need to articulate what a place
prioritization procedure is (Sarkar et al. 2002; Sarkar 2005:
159-68). In the abstract, place prioritization starts with a
list of places ("cells") and a list of surrogates that
must be represented at a certain target in a conservation area
network. We either want to select the smallest set of cells such that
every one of our surrogates meets it target, or select those cells
subject to a given size, which maximizes the number of surrogates at
or above the relevant target. Place prioritization is a component of a
consensus framework called systematic conservation planning that we
will discuss below. However, in effect, it is a procedure for
prioritizing places for their "biodiversity content" for
creating conservation area networks in which we incorporate
information regarding the design of the area. This can include the
shape of the network, its size, dispersion, and connectivity. This is
typically put more formally as follows (Sarkar 2005: 161). First,
suppose we begin with a set \(\Sigma\) of places or cells
\(\sigma\_{j}\); thus, \(\Sigma = \{\sigma\_{j} \mid \sigma\_{j} \in
\Sigma\}\) for \(j = 1, 2,\ldots, n\). Second, we have a set \(\Lambda\)
of surrogates \(\lambda\_{i}\); thus, \(\Lambda = \{\lambda\_{i} \mid
\lambda\_{i} \in \Lambda\}\) for \(i = 1, 2,\ldots,m\). Third, we have
targets for the representation of their expected coverage \(\tau\_{i}\)
for \(i= 1, 2,\ldots., m\). Place prioritization then takes the form
of two problems:


* Minimum Area: selection the smallest set of cell \(\Gamma\) such that
every surrogate meets their target of representation;
* Maximum Representation: given the cardinality \(\kappa \leq n\) of
\(\Gamma\), select those cells that maximize the number of surrogates
for which the expected coverage exceeds the targets of
representation.



The former problem has economic constraints but none on the number of
places whereas the second has a constraint on the total number of
places.



As we saw above, true surrogates consist in species, characters or
traits, life-zones, or environmental parameters. Estimator surrogates
consist in environmental parameter composition, soil-type composition,
dominant vegetation composition, subsets of species composition, and
subsets of genus or other higher taxon composition. So, biodiversity
is preserved by preserving true surrogates through preserving
estimator surrogates (Sarkar 2005: 168-73). To a first
approximation, a place prioritization procedure optimizes the number
of estimators and thus true surrogates with respect to place, and what
is so optimized is biodiversity. Biodiversity *just is* what is
optimized by this procedure.



There are several concerns worth mentioning with regard to how this
procedure implicitly defines biodiversity.
First, since we are operationalizing the concept biodiversity,
it is simply defined by what a place
prioritization procedure optimizes. Thus, it cannot be in error with
regard to whether it has captured some pre-theoretic notion. It rules
out the possibility of mistake (Hempel 1966: Ch. 7). We do not think
that the concept of temperature is just
whatever is measured by thermometers (Chang 2004). Second, insofar as
there are different place prioritization procedures, they will
implicitly define distinct concepts. They will not be measuring the
same magnitude. Third, concept explication requires that the
*explicatum* be distinct but sufficiently similar to the
*explicandum* for the analysis to be of theoretical value
(Carnap 1950/1962: Ch.1). There are two ways of seeing how the
concept(s) articulated by Sarkar differ from our "ordinary"
ones. The concept most conservation biologists articulate is
categorical and not relational. The concept is similarly imbued with
much moralizing as we shall see and one can be competent with the one
and not the other. Hence, one might argue that they are different
concepts (Odenbaugh 2009). As David Takacs writes,



> 
> 
> Biodiversity is the rallying cry currently used by biologists to draw
> attention to this crisis and to encapsulate the Earth's myriad
> species and biological processes, as well as a host of values ascribed
> to the natural world. (Takacs 1996: 9)
> 
> 
> 



At first blush, one might wonder if this is the same thing articulated
by place prioritization procedures.


### 1.3 Eliminating Biodiversity



Recently philosopher Carlos Santana has argued that we should
eliminate the concept of biodiversity from conservation biology
(Santana 2014). He writes,



> 
> 
> Biodiversity is generally the assumed target of conservation biology,
> but the biological world is composed of a number of distinct types of
> diversity, which only loosely correlate with each other and with
> biological value. Since the function of the biodiversity concept in
> conservation science is to help us preserve or increase biological
> value, we should therefore consider eliminating biodiversity from its
> privileged position in conservation theory and practice. (Santana
> 2014: 778)
> 
> 
> 



As we have seen, we begin with a notion of biodiversity which can be
estimated by a true surrogate but which actually is empirically
tracked through a estimator surrogate. However, Santana points out
that we are concerned with biodiversity because of the
"biological values" associated with it. This leads to two
sequences (Santana 2014: 765):


1. estimator surrogate \(\rightarrow\) true surrogate \(\rightarrow\)
biodiversity \(\rightarrow\) biological value
2. estimator surrogate \(\rightarrow\) true surrogate \(\rightarrow\)
biological value



Santana contends that (2) is preferable to (1). Why does he think
this? He argues that both Maclaurin and Sterelny's and
Sarkar's respective attempts at defining
'biodiversity' fail. Moreover, since according to Santana,
using the concept in conservation biology does more harm than good, we
should eliminate its use.



First, Santana argues that there are many different sorts of diversity
of biological value that cannot be subsumed by any single true
surrogate like species richness (Santana 2014: 768-9). For
example, we often wish to conserve biomes such as old growth forests
or coral reefs independent of their composition of species. Or, we
might want to conserve distinct salmonoid populations even if they are
of the same species. Second, assuming that there is a single or small
number of true surrogates, such as species richness, ignores the fact
that not all species are of equal biological value. A species of
pollinator or top predator like the grey wolf is of greater than value
than some species of minnow. Additionally, there are values that are
only tendentiously connected to species richness like
"wildness" or aesthetic value. Third, some species are of
great disvalue. For example, many would argue that taxa like Ebola
would be better extinguished. If this is correct, Santana contends
that there is no single true surrogate through which biodiversity can
be articulated. We must eliminate the notion of biodiversity.



The second part of Santana's argument is this pluralism is
unproblematic only if there is some true surrogate that covaries with
the other surrogates and hence biological value (Santana 2014:
768-73). However, he complains that the available evidence
suggests this is false. If we augment species richness with abundance
we will not capture certain biological values since, for example, the
rarity of a bird species increases its value and does not reduce it.
Phenotypic variation requires the sort of morphospaces discussed
above, but these as we saw are local and cannot be compared across
very different taxa. Hence, even if we augment species richness with
phenotypic variation we are unable to determine the biodiversity
present in comparable ways. Likewise, evolutionary diversity of the
sort that Faith described does not necessarily correlate with species
richness. In one study of the South Africa's cape region, it was
determined that the eastern region of the cape had greater
evolutionary diversity as measured by the number of speciation events
between clades whilst the western region of the cape had greater
species richness (Mooers 2007). Speciation events are often
unrecoverable from the fossil record and so this sort of diversity
cannot typically augment species richness. Lastly, as we noted above,
the connection between species richness and ecological properties like
stability is extremely complicated and controversial. It has not been
show in a robust manner that greater species richness leads to greater
ecosystem or food web stability.



Thus, there are many different types of diversity encoded in the sorts
of true surrogates we would like to conserve and they do not
positively correlate in straightforward ways. If this is right,
Santana contends that the concept of biodiversity in effect is not
tracking a feature of the biological world. At most it is a
placeholder for whatever conservation biologists choose to conserve.
The concept has certainly made its way into public discourse and one
might worry that this would be extremely disruptive if we removed it
from conservation practice. However, Santana suggests we should not
try to decide what to conserve based on some elusive property of
biological systems, which really is just a disjunction of very
different diversities. Rather, the tools through which value conflicts
are best articulated is from political science and economics (Colyvan,
Justus, & Regan 2011; Frank & Sarkar 2010). Regardless of the
tools used, Santana argues that we should bypass the concept of
biodiversity and consider the biological values that we share and
evaluate true surrogates on those grounds.



The discipline of conservation biology has settled on biodiversity as
its purported object of study. However, there is a serious debate over
whether biodiversity is some natural possibly multidimensional feature
or whatever is arrived at through procedures proposed in designing
conservation area networks. And, some even have argued that it should
be eliminated from scientific practice. It is possible by taking the
'logical' out of 'biological diversity' we
have a politically expedient term which is scientifically
problematic.


## 2. What is Conservation Biology?



Whether or not there is a single thing that conservation biology as a
discipline purports to conserve, there are philosophical questions to
be raised regarding the discipline itself. Specifically, is it a
collection of theories or models or something else? To explore this
question, we will consider central examples of conservation
biology's practice. We can then turn to how best to understand
what conservation biology is. But first, it useful to consider what
theories and models are.



Traditionally, philosophers of science have typically focused on
theories, which they assumed were sets of natural laws (Hempel 1966)
(see entry on
 laws of nature).
 Examples of laws of nature include the following. Newton's
universal law of gravitation is \(F\_{ab} = G(m\_a m\_b)/r^2\) which
states that the gravitational force between two objects \(a\) and
\(b\) is proportional to their masses and inversely proportional the
square of their distance. Newton's second law of motion is
\(\mathbf{F} = m\mathbf{a}\) where the vector sum of forces
\(\mathbf{F}\) equals mass \(m\) times acceleration vector
\(\mathbf{a}\). Finally, there is the Boyle-Charles ideal gas law
which is \(PV = nRT\) which says that the pressure \(P\) and volume
\(V\) of a gas is proportional to its temperature \(T\). Philosophers
of science and metaphysicians debate what makes statements like those
above laws. The answers include generalizations concerning empirical
regularities (Ayer 1956), relations between universals (Dretske 1977),
the best system that trades simplicity in primitives for explanatory
power (Lewis 1983), encode various symmetries (Fraassen 1989), and so
on. One powerful worry with thinking that theories are sets of laws of
nature is that if, at a minimum, laws are true universal
generalizations, then there no such laws at least in the special
sciences since those generalizations have exceptions (Beatty 1995;
Cartwright 1983). That is, they are false. As the result of this
argument amongst others, philosophers of science have increasingly
focused on models (Giere 2010; Odenbaugh 2005; Weisberg 2012) (see
entry on
 models in science).



Models are a type of representation that involve abstractions and
idealizations (Cartwright 1999; Chakravartty 2001; Weisberg 2012).
Abstraction in a representation involves not including all of the
properties of the thing represented and idealization involves
distorting those features that are represented. That is, models delete
and distort. Another important feature of models is that they
indirectly represent the world. According to many philosophers of
science, models describe mathematical or abstract objects, and these
objects are more or less similar to particular objects in the world
(Weisberg (2012), but see Hughes (1997; Odenbaugh 2014)). Theories as
sets of laws represent the world directly because their propositional
content is about natural systems. There are a number of different
types of models used which include physical, scale, computational, and
mathematical models.



In the biological sciences especially, one rarely hears talk of laws;
rather, it is models that are discussed. We can begin by considering a
simple model used in conservation biology, a metapopulation model
(Hanski & Gilpin 1997; Levins 1970). A metapopulation is a
population of populations that are subdivided spatially, but are
causally connected through migration. Let \(P\) be the proportion of
patches occupied by a species, \(c\) is the rate of patch
colonization, and \(e\) is the rate of patch extinction. Thus, the
instantaneous rate of change in the proportion of occupied patches is
the proportion of patches colonized minus those in which extinctions
occur. Mathematically, we have,

 

\[
\frac{dP}{dt} = cP(1 - P) - eP
\]


When \(dP/dt=0\), the non-trivial equilibrium proportion is \(P^{\*}=
(c-e)/c = 1 - e/c\). Thus, if the equilibrium proportion is greater
than zero, then the rate of colonization must be greater than the rate
of extinction; namely, \(P^{\*} > 0\) just in case \(c > e\).
Given that this is a model, there are abstractions and idealizations
including the assumptions that the local populations are identical in
their chances of colonization and going extinct, rates of colonization
and extinction are constants, and distance between patches does not
matter. Models can be characterized by three features--their
parameters, variables, and laws (Otto & Day 2007). Parameters
describe properties that are constant in value, variables describe
properties that change, and laws simply describe relationships between
parameters and variables. Thus, \(P\) is a variable, \(c\) and \(e\)
are parameters, and the equation \(dP/dt = cP(1- P) - eP\) is law.
Note that the term 'law' used by modelers is not
necessarily the same as that of a law of nature. For example, a
modeler might call the differential equation above a law even if it is
not true of any metapopulation.



Conservation biologists use models all the time in almost every facet
of their work. So we now consider several case studies of conservation
biology.


### 2.1 Northern Spotted Owls



Metapopulation models are extremely important and have been used in
one of the most difficult of environmental debates, that surrounding
the northern spotted owl (*Strix occidentalis caurina*) and its
habitat (Durbin 1996; Norse 1990; Yaffee 1994). The northern spotted
owl is a subspecies that lives exclusively in old-growth coniferous
forests in northern California, western Washington and Oregon.
Northern spotted owls are monogamous and each breeding pair uses one
to three square miles of forest that is at least two-hundred and fifty
years old. Farms, logging and other forms of development reduced
northern spotted owl habitat. So, scientists and policymakers began to
ask what must we do if we are to prevent them from going extinct? One
way of answering that question is to use metapopulation models.



In 1985, environmental lawyer Andrew Stahl presented mathematical
biologist Russell Lande just this question Yaffee (1994: 98). He
showed Lande the US Forest Service's "regional
guide" which was their plan to protect the northern spotted owl
and asked Lande to determine if it would be sufficient. Using a
variety of data, he devised the following metapopulation model to try
to answer this question (Lande 1988a but see Lande 1988b as well). Let
\(\epsilon\) be the probability that a juvenile female inherits her
mother's territory, \(m\) be the number of territories a
juvenile can disperse through before dying, \(h\) be the proportion of
habitable territory, and \(p\) be the proportion of occupied habitable
sites. Therefore, the probability of a female not finding habitable
territory in \(m\) trials is,

 

\[
(1-\epsilon)(ph+1-h)^m
\]


This just is the probability of not inheriting the territory of
one's mother and not successfully dispersing to an unoccupied
territory before death. Female northern spotted owls reproduce only
when they are three years old and we can represent their growth rate
by \(\lambda = N\_{t+1}/N\_t\). When \(\lambda = 1\), the species is in
a demographic equilibrium. Assuming they are in such an equilibrium,
then,

 

\[
[1-(1-\epsilon)(ph + 1 - h)^m] R'\_{0} =1
\]


\(R\_{0}' = \sum\_{x=0}^{\infty}l\_{x}'f\_x\) where \(l\_{x}'\) is the
probability of surviving to age \(x\) assuming she has found habitable
territory and \(f\_x\) is the mean lifetime offspring production per
female assuming she finds habitable territory. Finally, we can solve
the following,

 

\[
\hat{p} = \left\{
 \begin{array}{lr}
 1 - \frac{1-k}{h} & \text{for} \ h > 1 - k\\
 0 & \text{for} \ h \leq 1 - k
 \end{array}
 \right.
\]


In the above, \(k\) is the equilibrium occupancy of the territory.
Given this model, the northern spotted owl can persist if, and only
if, \(h > 1- k\). Lande determined that in 1987, 38% of forest in
western Washington and Oregon was older than two hundred years. So,
\(h = 0.38\). Fieldwork suggests that 44% of sites were occupied.
Thus, \(p = 0.44\). We can determine \(k\) from this equation,
\(k=1-h(1-p)\). Hence, \(k = 0.79\). The forest plans suggested
leaving between \(7 - 16\%\) of 200 or more year old forest. But given
that \(1 - k = 0.21\), Lande argued \(7 - 16\%\) was insufficient and
the forest plans should be revised. Lande and others' work led
to the US Forest Service withdrawing six old growth forest timber
sales in Oregon and Washington and became a component of the 1994
adoption of the Northwest Forest Plan that protected 8 million acres
of old-growth forests. This was an increase to protecting 80% of the
owl's remaining habitat in comparison to 5% protected by the
Forest Service's original owl plan.



Models like Lande's, and population viability analysis (PVA)
more generally, often are susceptible to several criticisms (see
Beissinger & McCullough (2002) for a discussion of PVA, and
Wimsatt (1981) for a discussion of the robustness and fragility of
models). They exhibit parameter and structural sensitivity; if we
change the parameter values or change the assumptions of the models,
we often find very different results. For example, Patrick Foley
examined a model of grizzly bears *Ursus arctic* in Yellowstone
National Park that incorporated environmental stochasticity along with
intrinsic growth rates and carrying capacity (Foley 1994). The model
for estimated values of the above predicted the expected time to
extinction was 12, 000 years. However, he later examined a model which
included demographic and environmental stochasticity but set
demographic stochasticity to zero (Foley 1997). The expected time to
extinction was approximately 50 years. Thus, PVA models can be very
fragile with respect to their parameter values and functional
forms.



Of course, Lande's metapopulation model is clearly abstracted
and idealized. However, he argued that his model was consistent with
the data and that his predictions were more conservative than that of
a more realistic model. First, he argued that the data showed that the
annual geometric growth rate of the northern spotted was not
significantly different from equilibrium (e.g., \(0.96 \pm0.03\), see
Lande 1988a: 605). Second, if we assume that owls have difficulty
finding mates, or dispersal is more limited, or there is demographic
or environmental stochasticity, this would reduce \(k\) even
further.


### 2.2 Island Biogeography



Island biogeography concerns the distribution and abundances of
species on islands. One of the fundamental patterns the biologists
have attempted to explain is the species-area effect. That is, why do
larger islands support more species than smaller ones? It seems that
as the area of an island increases, so does species richness. The
species-area effect is the product of the area effect namely that more
species are found on larger islands rather than smaller, and the
distance effect namely that islands closer to the mainland have more
species than those that are further. E. O. Wilson and Robert H.
MacArthur provided one of the famous potential explanations of the
species area effect. They did so through their equilibrium model of
island biogeography (MacArthur & Wilson 1963, 1967). They began
with several assumptions. First, species richness on an island has a
stable equilibrium. Second, the stable equilibrium is due to the
immigration rate from the mainland to the island and the extinction
rate on the island. Third, the distance of the island from the
mainland by itself determines the rate of immigration. Fourth, the
extinction rate is determined solely by the island's size.
Fifth, the stable equilibrium is a dynamic one. The number of species
are the island is constant, but the composition of species on the
island changes. This rate of change is the species turnover rate.



MacArthur and Wilson formulated a mathematical model with these
assumptions (Wilson & Bossert 1971). Suppose \(P\) is a parameter
representing a pool of species. Let the total immigration rate
\(\lambda\_{S}\) be the number of new species colonizing an island per
unit time. Let the total extinction rate \(\mu\_{S}\) be the number of
species on the island going extinct per unit time. Thus, the
instantaneous rate of change of species on the island is,



\[
\frac{dS}{dt} = \lambda\_{S} - \mu\_{S}
\]


If \(\lambda\_{S} = \mu\_{S}\), then \(dS/dt = 0\), and we have an
equilibrium \(S^\*\). Assume that \(S\) species are present on our
island. We characterize the total immigration rate as follows. Let
\(\lambda\_{A}\) be the average immigration rate of new species
colonizing the island per unit time, and thus the total immigration
rate is \(\lambda\_{S} = \lambda\_{A}(P-S)\). The total extinction rate
can be characterized as well. Let the average extinction rate per
species per unit be \(\mu\_{A}\). Then \(\mu\_{S} = \mu\_{A}S.\)
Therefore, \(dS/dt = \lambda\_{A}(P-S) - \mu\_{A}\). Finally, we can
derive \(dS/dt\) evaluated at the equilibrium \(S^\*\),

 

\[
\Bigl.\frac{dS}{dt}\Bigr|\_{\substack{S=S^\*}} = \lambda\_{S} - \mu\_{S} = \lambda\_{A}(P-S) - \mu\_{A}S = 0
\]


This is equivalent to the following expression

 

\[
S^\* = \frac{\lambda\_{A}P}{\lambda\_{A} + \mu\_{A}}
\]


By integrating the differential equation, we have,

 

\[
S^\* = \frac{\lambda\_{A}P}{\lambda\_{A} + \mu\_{A}}(1 - e^{-(\lambda\_{A} + \mu\_{A})^{t}})
\]


As \(t\) increases, \( e^{-(\lambda\_{A} + \mu\_{A})^{t}}\) approaches
zero.



We can also derive an expression for the species turnover rate.
MacArthur and Wilson as we saw thought that though the number of
species on an island would be constant, their identities would be
constantly changing. Suppose we are interested in some fraction say
90% of the equilibrium number of species \(S^\*\). Then, we multiply
both sides of the above by \(0.9\), and have \(0.9S^\* =
\lambda\_{A}P/(\lambda\_{A} + \mu\_{A}) \times 0.9\). From the above, it
follows that \((1 - e^{-(\lambda\_{A} + \mu\_{A})^{t}}) = 0.9\). With
some algebra and taking the natural logarithms, we arrive at the
species turnover rate,

 

\[
t\_{0.9} = \frac{2.3}{\lambda\_{A} + \mu\_{A}}
\]


Finally, let us assume that each species \(S\_i\)'s size is
proportional to the island's area and the probability that a
species goes extinct decreases as island size becomes smaller.




![[3a: a first quadrant graph with the y-axis labeled 'rate' and the x-axis labeled 's'. Two diagonal lines go from the upper left and the mid-upper left to the same point on the x-axis at the lower right. The upper left one is labeled 'near' and the mid-upper left is labeled 'far'. Two lines go from the origin at approximately 60 and 30 degrees. The 60 degree line is labeled 'small' and the 30 degree line is labeled 'large'. At the intersection of 'small' and 'near' lines a diagonal line drops to the x-axis. Another diagonal line goes from the intersection of 'large' and 'near'. Solid lines drop from the intersection of 'far' and 'small' and 'far' and 'large'.]](fig3a.png)

A




![[ 3b: a first quadrant graph with the y-axis labeled 'I' and the x-axis labeled with a circumflexed s. A diagonal line goes from the upper left to lower right and the x-axis intersection is labeled 'P'. Another line goes from the origin at about 45 degrees. A dash line goes from point 'P' to the 45 degree line and at the intersection is labeled 'E'. From the intersection of the diagonal line and the 45 degree line another dash line drops to the x-axis. That line is labeled 'x'.]](fig3b.png)

B





Figure 3: Equilibrium model and
explanation of the species area effect (MacArthur & Wilson 1967:
26)





Where the immigration rate \(I\) and extinction rate \(E\) cross is
the species equilibrium \(S^\*\) (Figure 3B). Moreover, the island
whose immigration rate is \(I\_\text{near}\) and whose extinction rate
is \(E\_\text{large}\) has the largest species equilibrium. The one
with the smallest is the one with \(I\_\text{far}\) and
\(E\_\text{small}\) (Figure 3A).



After constructing their model, E. O. Wilson and his graduate student
Daniel Simberloff tested the model by devising natural experiments in
the Florida keys (Simberloff & Wilson 1970). They had islands
defaunated of varying sizes and distances from the mainland. They
could then evaluate the predictions and assumptions of the model. They
determined that species abundances returned to their previous
equilibria and species richness on an island appeared to be determined
by the size of the island and its distance from the mainland. However,
Simberloff was able to show that the equilibrium model predicted that
the species turnover rate was much higher than what was measured
(Simberloff 1976). In some ways, it is not a surprise that the model
was predictively inaccurate since it was highly idealized. MacArthur
and Wilson had assumed distance alone determines the immigration rate
and size alone determines the extinction rate. MacArthur and Wilson
wrote,



> 
> 
> We do not seriously believe that the particular formulations advanced
> in the chapters to follow will fit for very long the exacting results
> of future empirical investigation. We hope instead that they will
> contribute to the stimulation of new forms of theoretical and
> empirical studies, which will lead in turn to a stronger general
> theory.... (MacArthur & Wilson 1967)
> 
> 
> 



But conservation biologists quickly embraced the model and began to
apply it to new topics, the most important of which was reserve
design.



Several conservation biologists, but especially Jared Diamond, argued
that single large reserves were better than several small ones (SLOSS)
(Diamond 1975a,b; May 1975; Terborgh 1975; Wilson & Willis
1975).



![[6 rows, in the center of each row is a letter, A through F descending. In the A row is a large circle on the left and a medium circles on the right. The B row has a medium circle on the left and 4 small circles in a compact 2x2 array. The C row has the compact 2x2 array of small circles on the left and a far less compact 2x2 array of small circles on the right. The D row has 3 small circles in a compact triangular formation on the left and 3 small circles in a row on the right. The E row has 3 small circles in a row connected by corridors on the left and 3 unconnected circles in a row on the right (same as D's right). The F row has a small circle on the left and a wide but vertically narrow blob on the right]](Diamond.jpeg)

Figure 4: Principles as depicted in the
SLOSS debate (Diamond 1975b: 174)




The principles debated included (A) larger reserves are better than
smaller ones, (B) a large reserve is better than a few small ones of
the same total area, (C) several reserves close to one another are
better than several further apart, (D) when there are several reserves
they should be grouped equidistantly rather then linearly, (E)
reserves connected by corridors are better than unconnected ones, and
(F) compact shapes are better for minimizing boundary length (1975b:
143-5).



Often the relation between species and area is represented by the
following equation, \(S = kA^z\) where \(S\) is species richness,
\(A\) is area, and \(k\) and \(z\) are constants (\(z\) is usually
between 0.2 and 0.35). Clearly, as \(A\) increases other things being
equal, \(S\) should increase as well. So, it was argued that larger
reserves are better for conserving species than smaller ones. Note
that if the reserve is isolated from other such reserves then only the
extinction rate matters according to the equilibrium model. In 1976,
Daniel Simberloff and Lawrence Abele argued that this line of argument
was incorrect (Simberloff & Abele 1976). Suppose we have a single
reserve of size \(A\_1\) and juxtapose it against two with size \(A\_2 =
A\_1/2\) each and that \(z = 0.263\) (this precise value is not
important to the argument). Each of the two small reserves would have
\(S\_2 = kA\_{2}^z\) species. If all of the species \(P\) in the pool
are capable of dispersing in and surviving in the refuges, then the
total expected number of species in both refuges is \(2S\_2 -
S\_2^2/P\); namely the species in either small reserve minus those in
both. How many species \(S\_1\) would be in the large reserve \(A\_1\)?
Simberloff and Abele determined that \(S\_1 = kA\_1^z = k(2A\_2)^z =
1.200 \times S\_2\). They pointed out that this is less than the total
expected number of species \(2S\_2 - S\_2^2/P\) in the two smaller
reserves when \(S\_1/P \lt 0.96\). Another important criticism of the
use of the equilibrium model in reserve design was that reserves were
unlike islands in an ocean (Margules, Higgs, & Rafe 1982).
Specifically, the equilibrium model assumes that between the mainland
and island no species of the pool can exist; i.e., the immigration
rate is zero. However, when we consider areas between reserves, it
often is possible for species to occur even if at lower abundances.
Thus, the equilibrium model could not be extended without violation of
its assumptions to other habitat types such mountain tops (J.H. Brown
1971).



The SLOSS debate was vociferous and the arguments continued for quite
some time. The importance of the equilibrium model of island
biogeography was much discussed and hotly contested (R.J. Whittaker
& Fernandez-Palacios 2007).


### 2.3 Systematic Conservation Planning



Though the types of models we have just discussed have an important
place in the conservation biology and its history, it has become an
explicitly socio-ecological discipline in which sophisticated
computational tools are used for the purpose of designing conservation
area networks (see entry on
 social choice theory).
 From work involving population genetics and ecology for population
viability analysis and the equilibrium model of MacArthur and Wilson,
with the associated SLOSS debate, we see a discipline transformed.
Work on the genetics of inbreeding, habitat fragmentation,
metapopulation dynamics, and so on continues but in the guise of
something more social; namely, systematic conservation planning (SCP)
(Margules & Pressey 2000; Margules & Sarkar 2007; Watson et
al. 2011). SCP involves a variety of steps including the
following:


1. Choose and delimit the planning region.
2. Identify all stakeholders.
3. Compile and assess all data.
4. Treat data and construct models as necessary.
5. Identify and evaluate biodiversity constituents and
surrogates.
6. Set explicit biodiversity goals and targets.
7. Review existing conservation areas for performance with respect to
targets.
8. Prioritize additional areas for conservation management.
9. Assess biodiversity constituent and selected area
vulnerabilities.
10. Refine the network of selected areas.
11. Carry out multi-criteria analysis.
12. Implement conservation plan.
13. Monitor network performance (Sarkar 2012: 100-3)



There are a variety of philosophical questions that are raised by SCP.
First, who are the stakeholders in determining the boundaries of the
analysis, what are the relevant criteria to be used, and what is the
focal biotic unit? Second, what is biodiversity and what are the
biotic constituents we are trying to conserve and ensure persist?
Given problems with current definitions of 'biodiversity'
as we saw above, we must articulate what biodiversity constituents and
surrogates are, along with other diversity and persistence concepts.
Third, given resource scarcity, efficiency is one of the goals of SCP,
and thus how do we solve the minimum area and maximum representation
problems mentioned above? What is striking is how much the discipline
includes over and above models like the Lande metapopulation and
MacArthur and Wilson equilibrium models. As Sahotra Sarkar writes,



> 
> 
> Thus, in conservation biology, like computer science and unlike most
> of ecology, theoretical research consists of devising algorithms
> rather than formulating models and theories. In fact, because a
> variety of algorithms can be used to solve these problems, a lot of
> theoretical debate in conservation biology has been about the choice
> of algorithms. (Sarkar 2012: 124)
> 
> 
> 



We have seen the role of place prioritization, which is key to SCP. We
next consider one other formal tool that is used to help make
decisions about how to accommodate different values in choosing
conservation area networks.



One way to represent values would to be treat them as preferences
using the tools of neoclassical economics. Ideally we would reduce
these values to a single scale via a utility function. However, this
is possible only if these values are commensurable (orderable on
single scale). Some have argued that not all values can be so ordered.
For example, suppose you have to decide whether to be a lawyer or
clarinetist (Raz 1986: 342). Neither seems better than the other and
they do not seem to be of equal value. Thus, we cannot rank them on a
single scale. Nevertheless, if values are commensurable and we can
measure them through people's willingness-to-pay for them or
willingness-to-be compensated for their loss, then they can be
represented by a utility function. Multi-criteria analysis does not
make these two assumptions (Arrow & Raynaud 1986). Rather, the
preferred method is determining a set of non-dominated alternatives
(Moffett & Sarkar 2006). Suppose have a set of criteria \(K =
\{\kappa\_i \mid i = 1, 2,\ldots, n\}\) and a set of feasible
alternatives \(A = \{\alpha\_j \mid j = 1, 2,\ldots, m\}\) (for
details, see Sarkar 2005: 196-203). We will further assume that
each criterion \(\kappa\_i\) induces a weak linear order \(\leq^\*\) on
\(A\); that is, for any two \(\alpha\_m, \alpha\_n \in A, \alpha\_m
\leq\_i^\* \alpha\_n\) or \(\alpha\_n \leq\_i^\* \alpha\_m\) or both. Let
\(w\_{im}\) be the value of the \(m\)th alternative according to the
\(i\)th criterion. We can say then that an alternative \(\alpha\_m \in
A\) is *non-dominated* by another alternative \(\alpha\_n \in
A\) with respect to \(K\) if, and only if:

 

\[
\begin{aligned}
\exists p, k\_p \in K \textrm{ such that } \ w\_{pn} \lt w\_{pm}\\
\forall q, k\_q \in K, w\_{qn} \leq w\_{qm}
\end{aligned}
\]


This implies that \(\alpha\_n\) is strictly better than \(\alpha\_m\) by
at least one criterion in \(k\_p \in K\), and it not worse than
\(\alpha\_m\) by any criterion \(k\_i \in K\). An alternative \(\alpha\_i
\in A\) is *non-dominated* if, and only if, it is not dominated
by any other element of \(A\). Thus, using multi-criteria analysis, we
attempt to find a non-dominated set of alternatives from which to
design conservation area networks. An example from Sarkar (2012:
204-6) is briefly considered. We begin with place prioritization
with 100 attempts to create a prioritized lists of hexagons for a
target of 10 representatives for each vertebrate species present in
Texas. There were thirty-two feasible solutions--ones that
satisfied the conservation target. However, two other criteria were
used for multi-criteria analysis, economic cost and social cost. The
first was estimated by the cost of the conservation plan per hexagon
and the second was estimated by the human population size of each
hexagon included. From the thirty-two feasible solutions, there were
only two non-dominated solutions selected by the multi-criteria
analysis.



![[A graph captioned with 'Feasible and Non-Dominated Solutions', the y-axis is labeled 'Social Cost' and marked from 0.00E+00 to 8.00E+06 and the x-axis is labeled 'Economic Cost' and marked from 1.45E+08 to 1.80E+08. The legend has solid squares labeled 'Feasible Solutions' and solid circles labeled 'Non-Dominated Solutions'. The graph itself has the 28 or so squares scattered between 3.00E+06 and 6.00E+06 (on the y) and 1.50E+08 and 1.65E+08 (on the x). 2 squares are outliers, one at 7.00E+06 and 1.63E+08 (y,x) and the other at 6.00E+06 and 1.75E+08. The two circles are at 3.00E+06 and 1.46E+08 (y,x) and at 2.50E+06 and 1.55E+08.]](Sarkar.jpeg)

Figure 5: Place prioritization and
multi-criteria analysis in Texas (Sarkar 2012: 206)




Next we reflect on the case studies and then ask what exactly is
conservation biology?


### 2.4 Conservation Biology and the Philosophy of Science



Philosophers of science have thought of the core questions of the
discipline as these. What is the structure of scientific theories?
What is the logic of confirmation of theories by evidence? What is a
scientific explanation? What is scientific objectivity and does the
presence of values in science thwart that objectivity? With regard to
the Lande's metapopulation model and MacArthur and
Wilson's equilibrium model, we can ask the first three
questions. First, we can recognize that these are models that
abstracted and idealized representations of objects like northern
spotted owls and fauna on Mangrove islands in Florida. Likewise, we
can look at evidence that confirms and disconfirms them. Lande argues
that the assumption that northern spotted owls were at a demographic
equilibrium was supported by the available evidence; Simberloff argued
that measures of species turnover rates disconfirmed the equilibrium
model as applied to the fauna on the Mangrove islands. We can argue
whether there is sufficient evidence that the assumptions of the
equilibrium model apply to mountain tops as opposed to islands. One
might also argue that the equilibrium model explains the species area
effect. For those case studies the traditional questions are present
and philosophical answers to them can be articulated. We can also ask
whether Lande's work was sufficiently objective given that he
did the analysis for "political" purposes. However, when we consider SCP, things look very different. Consider place
prioritization procedures and multi-criteria analysis. The former
considers a set of cells, surrogates, and targets and tries to solve
to find the smallest set of cells such that every surrogate is met at
their target and find those places subject to a constraint for which
coverage exceeds the target. These are optimization problems to be
solved by an algorithm via a computer program. The latter considers a
set of alternatives and using a suite of criteria representing our
values, which determines a non-dominated set of alternatives to inform
design of conservation networks. This brings tools from economics,
policymaking, and operations research to help identify solutions to
problems. In fact, much of the work that is done in systematic
conservation planning is done using software packages like the
following:


* Reserve design (Marxan, Zonation, TNC Sites)
* Species distribution modeling (Maxent, DesktopGARP, DIVA-GIS,
openModeller, BioMapper),
* Connectivity (Linkage Mapper, Corridor Designer, CircuitScape,
Connectivity Analysis Toolkit, Conefor, UNICOR),
* Threat assessment (GeoCAT, Mirada),
* Population viability analysis (Vortex, BIOMOD, RAMAS),
* Multi-criteria analysis (EXPERTCHOICE, MultiCSync).



Thus, with regard to SCP, it would make little sense to ask are these
programs, their associated algorithms, and outputs "true"?
Are they confirmed or disconfirmed? How and what do they potentially
explain? If we inspect the list of computer programs above, we
see many different things that include more than just biology (see
entry on ecology). The tools include ones
drawn not just from the natural sciences, but the social sciences and
humanities as well. This raises the question to what extent is
conservation biology *biology*? Should it simply be
conservation *science* (Kareiva & Marvier 2011)?




There are two rival views as to what the central aims of science are.
Realists contend it is truth and empiricists say it is empirical
adequacy (see entry on
 scientific realism).
 Representing the realist's view, Bas van Fraassen writes,



> 
> 
> Science aims to give us, in its theories, a literally true story of
> what the world is like; and acceptance of a scientific theory involves
> the belief that it is true. (Fraassen 1980: 8)
> 
> 
> 



However, van Fraassen is an empiricist (see entry on
 constructive empiricism).
 For the empiricist,



> 
> 
> Science aims to give us theories which are empirically adequate; and
> acceptance of a theory involves as belief only that it is empirically
> adequate. (Fraassen 1980: 12)
> 
> 
> 



If SCP is science, then neither realism nor empiricism fit the main
aims of science as articulated by prominent philosophers of science.
If so much of conservation biology involves developing algorithms and
computer programs and articulating various conventions, then truth and
empirical adequacy are relevant for parts of the discipline but
irrelevant for large swaths. Moreover, if theory structure and
confirmation are irrelevant to those swaths because they concern truth
or empirical adequacy, then the topic of scientific explanation will
be irrelevant as well. Since there are few theories and models, then
the questions regarding Bayesian versus frequentist methods of
scientific inference will find less purchase in conservation biology
practice. Additionally, and maybe most important, socioeconomic values
are inputs into conservation biology and it simply cannot be done
without them. Or, if it is to be done, it will be the values of a
small set of scientists thus privileging the wrong people (Guha 1998,
Martin 2017). If SCP is central to conservation biology, much of
philosophy of science is irrelevant to conservation
biology. Conservation biology increasingly looks like a pragmatic or
instrumental endeavor. One possible response would be to develop a
pragmatist or instrumentalist philosophy of science which
de-emphasizes truth and empirical adequacy (Dewey 1938; Laudan
1978).



Let's consider how one might develop a more pragmatic philosophy
of science. Some philosophers distinguish between *epistemic*
and *non-epistemic* values (Kuhn 1977; McMullin 1982; Longino
1990, 1996; Lacey 1999; Laudan 2004; Douglas 2009, 2013; Steel
2010). The former concern truth and related notions like justification
and explanation whereas the latter concern topics like equality,
profit, health, safety, and the like. It can be very difficult to
clearly distinguish between these two types of values and attempting
to doing so can quickly take us into ethical and metaethical
issues. Nevertheless, in the sciences, we have epistemic questions
like "Do the theoretical terms of a model refer to existing
entities?", "Is this model approximately true or
empirically accurate?", "How accurate are the predictions
of this model?" Likewise, we have non-epistemic questions like,
"Is it easy to use this model?", "Is this hypothesis
accurate enough for our present purposes?", "Can this
theory provide results in a timely fashion?", "Is the
model relatively inexpensive to use?" (Elliott and McKaughan,
2014, 4-5). Thus, there seems to be both epistemic and
non-epistemic values in science even if the line between them is hard
to draw. Additionally, we often evaluate our theories and models with
non-epistemic considerations As Ronald Giere has argued,
"Scientists use models to represent aspects of the world for
specific purposes" (Giere 2004, 742). These scientific
representations are used for a variety of purposes. These purposes are
not only epistemic; some are non-epistemic. Moreover, there are
tradeoffs between epistemic and non-epistemic values or purposes. For
example, we can increase the predictive accuracy of a model by
increasing the number of adjustable parameters. However, this also
increases the amount of work needed to measure those parameters, and
it might be too costly. As a result, some philosophers of science
argue that non-epistemic values can have more importance than
epistemic values when they promote the aims of scientific inquiry
(Elliott McKaughan 2014; Hicks 2014; Intemann 2015).




One might object that the fundamental aims of science are epistemic
rather than non-epistemic or that epistemic values should constrain
non-epistemic ones (Douglas 2009; Steel 2010, 2015; Steel and Whyte
2012). Some still will push back. First, it is clear that practical
aims *do* outweigh epistemic aims at least
sometimes. Idealizations that distort the truthfulness of models may
be justified by practical considerations such as greater computational
efficiency or tractability. Second, insofar as non-epistemic values
outweigh epistemic ones, this can be carefully reflected in
scientists' attitudes towards their theories and models. For
example, instead of believing that a given model is true, they may
accept it for predictions with regard to a given domain or for
policy-making in very specific scenarios. However, as Daniel Steel
argues, if scientific inquiry does not prioritize epistemic values,
then it is subject to what he calls *Ibsen predicaments* (after
Henrik Ibsen's *An Enemy of the People*). An Ibsen
predicament occurs when (a) highly valued objectives of a community
are linked to some activity, (b) this activity has seriously harmful
effects, and (c) if those effects became widely known and acted upon,
the community would be threatened (Steel 2017, 51).




It appears then that there are epistemic and non-epistemic values in
the sciences. Whether the latter should take precedence over the
former is the subject of current philosophical
discussions. Conservation biology might very well be such a science where
non-epistemic values should take precedence over epistemic
ones. Nevertheless, we are thus left with several questions. First,
how do we distinguish between epistemic and non-epistemic values in
the sciences? Second, if we can distinguish between them, are
non-epistemic values sometimes more important than epistemic ones?
Third, if non-epistemic values are sometimes more important than
epistemic ones, does this occur in conservation biology? Can these
non-epistemic values include *moral* and *political
values*? It is to issues concerning these values that we now turn.


## 3. What Are the Aims of Conservation Biology?



In this section, we will consider three questions. Is conservation
biology "value-laden"? What values are to be found in
conservation biology? Should conservation biologists be advocates of
these values?


### 3.1 Inductive Risk and Value-Ladenness



Scientific decision-making involves values. Some however have argued
that the values present are merely epistemic values (Laudan 1986). For
example, Thomas Kuhn argued that scientific theories are evaluated in
terms of accuracy, consistency, scope, simplicity, and fruitfulness
(Kuhn 1977). Moreover, he construed these as values because they are
so vague as to require interpretation and they are many and thus
require weighting. If they were merely methodological rules, then
these would not be problems. Nevertheless, they appear to be
epistemic. Conservation biology serves as an interesting case because
it appears that moral and sociopolitical values are to be found at its
core (Longino 1990). That is, they are not only epistemic.



Let consider an argument from inductive risk (Douglas 2000; Hempel
1965; Rudner 1953, and in the context of conservation biology see
Shrader-Frechette & McCoy 1993: Ch. 6). In standard Neyman-Pearson
hypothesis testing we formulate a null hypothesis \(H\_0\) and an
alternate \(H\_1\) (Gotelli & Ellison 2013). The null states that,
"\(C\) does not have an effect \(E\)". The alternate is
the negation, which states that "\(C\) has an effect
\(E\)". Thus there are two relevant probabilities of error.





|  |  |  |
| --- | --- | --- |
|  | \(H\_0\) is true | \(H\_0\) is false |
| Accept \(H\_0\) | Correct | Type II error |
| Reject \(H\_0\) | Type I error | Correct |



Table 1: Type I and Type II errors




We can also represent them as,

 

\[\begin{aligned}
\Pr(\text{Rejecting } H\_0 \mid H\_0 \text{ is true}) &= \alpha\\
\Pr(\text{Accepting } H\_0 \mid H\_0 \text{ is false}) &= \beta
\end{aligned}\]


Suppose we have the following null and alternate hypotheses (Noon and McKelvey 1996, Noon and Blakesley 2006).



> 
> 
> \(H\_0\): Logging old growth forest will not reduce northern spotted
> owl populations
>   
> 
> \(H\_1\): Logging old growth forest will reduce northern spotted owl
> populations
> 
> 
> 



Thus there are two errors again. We reject \(H\_0\) and log old growth
forests will not reduce northern spotted owl populations. We accept
\(H\_0\) and logging old growth forests will reduce northern spotted
owl populations. For mathematical reasons, we cannot minimize both
Type I and Type II errors, and thus we must minimize \(\alpha\) or
\(\beta\). By custom, scientists attempt to minimize Type I errors;
that is,



> 
> 
> Reject \(H\_0\) if, and only if, \(\Pr(\text{Rejecting } H\_0 \mid H\_0
> \text{ is true}) \lt 0.05\)
> 
> 
> 



However, this requires that we evaluate which would be
worse--allowing logging though it reduces the owl population or
not allowing logging though it would not have reduced them anyway?
That is, we have a moral or sociopolitical question. Given habitat
protection affects both the owls and the livelihoods of loggers living
in the Pacific Northwest, this is not merely a technical scientific
question. Some have argued that when environmental harms are at stake,
we should minimize Type II errors (Shrader-Frechette & McCoy
1993). This mode of reasoning is sometimes called the
"precautionary principle" (Steel 2015).



Neyman-Pearson statistical hypothesis testing is a very common
methodology taught in statistics courses and used by practicing
biologists. However, some philosophers and statisticians reject it for
an alternative, Bayesianism (Howson & Urbach 2006). According to
Bayesianism, we neither accept nor reject hypotheses (though one can
work out a notion of acceptance in the framework (see Levi 1974; Maher
1993)). Rather, we compare the probability of hypothesis with regard
to some evidence and determine how it affects its probability (see
entry on
 philosophy of statistics).
 Whether the problem of inductive risk disappears in a Bayesian
inferential frameworks is an interesting question. Insofar as
credences (i.e., subjective degrees of belief) are affected by
attitudes towards risk, then arguably the problem arises in a
different guise.



Whether one finds the argument from inductive risk persuasive, it is
clear that ethical and sociopolitical values are brought into the
science itself by various scientists (see entry on
 scientific objectivity).
 Conservation biologists such as Paul Ehrlich, E. O. Wilson, Thomas
Lovejoy, etc. often advocated ethical values as scientists in order to
raise awareness regarding ecological degradation and its effects on
species including our own (Takacs 1996). Supposing that there are
epistemic and moral values in conservation biology, what are the
specific ones present? One way to taxonomize those values is through
Helen Longino's work (Longino 1990) (see entry on
 feminist epistemology and philosophy of science).
 She draws a distinction between *constitutive* and
*contextual* values in science. Constitutive values are those
that are essential to the scientific inquiry. For example, if Kuhn is
correct that accuracy, simplicity, and scope are values and they are
essential to scientific methodology, then these would be constitutive
values. You simply cannot do science without them. Contextual values
are those that are accidental to science; they come from the personal,
social, and cultural context in which science occurs. It worth noting
the epistemic/non-epistemic values distinction is related to but
distinct from the constitutive/contextual value distinction. For
example, one might argue honesty is a constitutive ethical value of
scientific inquiry (Pennock 2019). Likewise, one might argue that
certain scientists have differing contextual epistemic commitments
(Segerstrale 2000). 



Some of these contextual values arise from individuals because of
their own commitments. E. O. Wilson has written a great deal arguing
that we should work against anthropogenic species extinction for
example (Wilson 1992). Group contextual values include 
codes of ethics. For example, the Society for Conservation Biology has
a Code of Ethics (see the Other Internet Resources). It includes
obligations like,



> 
> 
> Actively disseminate information to promote understanding of and
> appreciation for biodiversity and the science of conservation biology.
>   
> 
> Recognize that uncertainty is inherent in managing ecosystems and
> species and encourage application of the precautionary principle in
> management and policy decisions affecting biodiversity.
> 
> 
> 



Though one might argue about specific obligations included in the Code
of Ethics, generally we the public agrees that one should act with
integrity in one's research and treat non-human animals with
respect. Still, there are more controversial societal values that play
a part in conservation biology. Conservation biologist Reed Noss
writes,



> 
> 
> Conservation biology has been described throughout its history as
> "value-laden", "mission-oriented",
> "normative", and sometimes in less flattering terms. The
> entire field rests on the value assumption that biodiversity is good
> and ought to be conserved. Human actions that protect and restore
> biodiversity are good; those that destroy or degrade biodiversity are
> bad. (Noss 2007: 18)
> 
> 
> 



Recently, many human lives have been lost to the virus *Zaire
ebolavirus* in the genus *Ebolavirus*. To exterminate this
virus or the genus one might argue is not morally wrong but morally
permissible if not obligatory. Noss' claim is that this is
morally wrong, which many would find controversial.



Consider the claim that biodiversity has intrinsic value (see entry on
 environmental ethics).
 Should conservation biologists accept this? Michael Soule
famously claimed that conservation biology included a variety of
"normative postulates" including: diversity of organisms
is good, ecological complexity is good, evolution is good, and biotic
diversity has intrinsic value (Soule 1985: 730-1). He
writes,



> 
> 
> This normative postulate is the most fundamental. In emphasizing the
> inherent value of nonhuman life, it distinguishes the dualistic,
> exploitive world view from a more unitary perspective: Species have
> value in themselves. a value neither conferred nor revocable, but
> springing from a species' long evolutionary heritage and
> potential or even from the mere fact of its existence. (Soule
> 1985: 731)
> 
> 
> 



First, what does it mean to say biodiversity has intrinsic value
(Vucetich, Bruskotter, & Nelson 2015)? Ethicists debate what is
intrinsic value and what might have it (O'Neill 1992). Some
argue that intrinsic value is the value something has in virtue of its
intrinsic properties (Moore 1903) A property is intrinsic roughly if
it is had by something independent of other objects (see entry on
 intrinsic vs. extrinsic properties).
 However, many properties that make species valuable include ones like
beauty and rarity which are arguably extrinsic (Russow 1981). Other
philosophers argue that intrinsic value is what we value for its own
sake (Ewing 1947; Lewis 1989). What is special about intrinsic value
is how something is valued not the properties of thing so valued. But
this is says nothing about what the attitude of valuing is and it
raises other difficult questions. Some think intrinsic value does not
depend on valuers whatsoever. But this appears to assume a strong form
of moral realism about value, which some metaethicists would deny
(Blackburn 1993; Gibbard 1992).



Second, conservation biologists themselves do not agree on the matter.
Here are representative quotations from conservation biologists on the
subject (as quoted in Takacs 1996: 249-252):



> 
> 
> David Ehrenfeld: "For biological diversity, value *is*.
> Nothing more and nothing less.... Well, I couldn't prove
> it, I guess. I just believe it."
> 
> 
> 
> Paul Ehrlich: "... I just can't have the feeling that
> the only value they [species] might have is what they might mean to
> us. But you can't possibly defend that
> scientifically".
> 
> 
> 
> Jerry Franklin: "Oh, I basically think so, yes. But I
> haven't given a whole lot of thought to it".
> 
> 
> 
> Daniel Janzen: "The word value is anthropocentric....
> That's a contradiction in terms".
> 
> 
> 
> S. J. MacNaughton: "I don't see how anything can have
> value outside of a value that human beings place on it, because value
> is really something uniquely human, isn't it?"
> 
> 
> 
> David Pimmentel: "[I]n trying to protect or conserve nature, to
> use the argument of intrinsic value gets you--well, I don't
> think it sells very well".
> 
> 
> 



If prominent conservation biologists disagree whether biodiversity has
intrinsic value and what it means to claim it, one might ask how it
could be axiomatic in the first place.


### 3.2 "New Conservationism"



Recently, Soule's vision has been called into question by
the "new conservationists". Peter Kareiva and Michelle
Marvier have argued for a different foundation for conservation
biology or what they call "conservation science" (Kareiva
& Marvier 2012). They argue that the science needs a new normative
direction for a variety of reasons. First, conservation biology has
traditionally focused on conserving natural systems for the intrinsic
value of these systems. However, they contend ecological systems can
only be successfully managed by embracing the instrumental values
provided to humans by these systems. Traditional conservation biology
they allege has been at the expense of third world stakeholders and
contrary to the interests of women. Second, we live on a planet that
has been deeply influenced by humans; so much so that many suggest we
live in a new geological epoch, the Anthropocene. Human population
stands at approximately 7 billion and our energy consumption has only
increased over time. Humans have deforested, urbanized, polluted,
extirpated, and overharvested to such a degree that there is no
"pristine" nature available. "Fortress
conservation" is an out-of-date model of what we should and can
do. Moreover, environmental attitudes have changed over time--we
value environmental goods and services less and less in the United
States. Thus, we must focus on what people care about and focus on
motivating them through these valuations. Third, ecological systems
are not as fragile as traditional ecology suggests. Rather, they are
remarkably resilient. Marine systems overcome oil spills, deforested
areas rebound, bird species dwindle but are rarely eliminated whole
cloth, and coral reefs can even come back from a hydrogen bomb.
Fourth, we can avoid the tragedy of the commons, which has been a
major theoretical structure for interpreting environmental
decision-making. Following the work of Elinor Ostrom, local buy-in can
eliminate the self-interested behavior that leads to such zero-sum
behavior (Ostrom 1990). To move forward, they recommend that we
recognize conservation can only occur within human-affected
landscapes; that we work with corporations and not merely oppose them;
and that we avoid threatening the human rights of the
disenfranchised.



As one would expect, the new conservationists have been forcefully
challenged. For example, David Doak, Victoria Bakker, Bruce Goldstein,
and Ben Hale have challenged their claims across the board (Doak et
al. 2014). First, they contend that Kareiva and Marvier focus on human
well-being in the most narrow economic terms. Moreover, conservation
has never been solely for the "more-than-human world" but
has a rich tradition of managing for human interests as well. Second,
there are many relatively undisturbed ecological systems that exist,
and others have suffered irreversible effects by humans (e.g.,
anthropogenic global climate change). Third, they suggest that a
narrowly anthropocentric approach to conservation has little by way of
evidence of success. And, traditional approaches have much that
testifies in their favor. Additionally, though some conservation
projects have displaced indigenous peoples, conservation organizations
have been working hard over the last few decades to try to avoid these
environmental injustices. Fourth, they contest the social science that
claims environmental attitudes have fundamentally changed and notes
that actors are rarely self-motivated and apathetic in the ways the
new conservationists suggest. Finally, if one were solely committed to
human well-being with little regard to the moral standing of
ecological systems, then we should be investing not in the Nature
Conservancy but Save the Children, Oxfam, and Water for the
People.



Consider a concrete debate between traditional conservation biologists
and the "new conservationists." Recently, some
conservation biologists have argued that we should make half of the
Earth's surface into interconnected protected areas, which they
claim would conserve about 85% of our remaining biodiversity (Noss
et. al. 2012; Wuerthener et. al. 2015; Wilson 2016). Currently, only
about 15% of the Earth's terrestrial areas and about 3% of its
oceans are protected. If we consider a variety of studies as to what
percentage of terrestrial regions should be protected to conserve
biodiversity, the median percentage is slightly below 50% (Noss et
al. 2012, 2). The "Half-Earth" plan offers three claims:
(a) habitat loss and degradation are crucially important causes of
biodiversity loss, (b) current protected areas are insufficient for
preventing further loss of biodiversity, and (c) it is morally wrong
for our species to cause species' extinction (Wilson
2016). Critics argue that "Half-Earth" has several flaws
Buscher et. al. 2017). First, it ignores the most
serious problems threatening biodiversity - resource extraction and
consumption. Second, if authorities created strict protected areas
(IUCN Category I or II), then it would have considerable negative
effects on indigenous people and communities. Third, it is likely that
control over these reserves will not be given to the indigenous people
and communities effected, which will only exacerbate their poverty and
vulnerability. Fourth, when local people are stakeholders in
conservation programs, both biological conservation and socioeconomic
outcomes are benefited. Fifth, "Half-Earth" offers no plan
for biodiversity conservation for the other half. They suggest that we
focus on the main drivers of biodiversity, which requires rethinking
free markets and economic valuation and moving towards regulation,
redistribution, and viewing natural resources and ecosystems as global
public goods. Additionally, they suggest our conservation plans should
explicitly address economic inequality (Mikkelson et. al. 2007;
Holland et al. 2009; Hicks et al., 2016). It is both harmful for
humans and leads to biodiversity loss itself. In favor of Half-Earth,
its defenders have argued in return that though Half-Earth is
necessary but insufficient for conserving *all* of
Earth's biodiversity. They also agree that we should reject
neoliberal economics. However, we should not prioritize equality for
humans over non-human species' equality; "This will
require setting aside sufficient habitat for other species while
living justly and prudently on the remainder" (Cafaro
et. al. 2017, 400). The Half-Earth proposal is very controversial, but
it is also emblematic of the debates within conservation biology.




Regardless, of how we view these contemporary debates, it is clear
that there are constitutive and contextual values present in
conservation biology and any successor discipline moving forward.
These include the relatively uncontroversial ones regarding inductive
risk and SCB's Code of Ethics, to those regarding the intrinsic
value of organisms, species, and ecosystems. Furthermore, there are
very important questions regarding how these values, non-controversial
and controversial, should be advocated in the public sphere. If the
values are broadly shared by scientist and non-scientist alike, then
it appears that advocacy is simply on the public's behalf.
However, as the values become specific to the scientists themselves,
their advocacy can appear to be in tension with regard to their
objectivity as scientists (M.J. Brown 2013; Intemann 2005; Longino
1990). They appear as "tree huggers" and
"environmentalists" and less than impartial. Moreover, if
their advocacy detracts from their role as scientists, they one might
worry that this all things considered renders them impotent in
policymaking. They seem to be just another "talking head"
(Odenbaugh 2003). Thus, conservation biologists must discern how to
balance the values at work in their discipline and the greater good to
which they are committed.



Some policy analysts argue that if scientific work is to impact
policy, it must be objective (Pielke 2007). However, advocating
personal contextual values is alleged to undercut that objectivity
(Burke & Lauenroth (2009), but see Strong (2008)). If one claims
that biodiversity is intrinsically valuable, this value isn't
widely shared, and it cannot be reasonably defended by its proponents,
then conservation biologists risk their work being ignored or
dismissed. On the other hand, if one advocates the conservation of
biodiversity on the basis of ecosystem services including carbon
sequestration, waste decomposition, purification of air and water,
pollination, etc., and loss of these services would negatively impact
humans, then this advocacy would be far less controversial (Brussard
& Tull 2007). In addition, one might reconsider how we think about
science itself. We recognize that the enterprise is not
"value-neutral". Should we regard it as less than
objective because of its advocacy (Kincaid, Dupre, & Wylie
2007)? How might we educate the public to think more carefully and
clearly about the role of values in science?


## 4. Conclusion



In this entry, we have consider three philosophical issues in
conservation biology. First, we began with the ontology of the
discipline--what is biodiversity? We examined three proposals:
biodiversity should be multidimensional, it should be operationalized,
and that the concept should be eliminated from conservation practice.
Second, we considered several different important cases studies in the
science--modeling northern spotted owls, the equilibrium model of
island biogeography and its impact on the SLOSS debate, and systematic
conservation planning. What is striking is how traditional
philosophical questions appear to disappear or be reoriented in recent
work. For example, though there are theories and models, the core
theoretical structures are different from other sciences since it
involves various conventions and algorithms. Third, we consider the
axiological foundations of the discipline. Many conclude that the
sciences involve considerations of value. However, conservation
biology was thought to be founded on the intrinsic value of ecological
systems though the recent new conservationists have challenged this
foundation as resting on soft sand. Critics, of course, have come out
in full force arguing that this critique misses its mark and the
foundations, though apparently shaky, are only apparently so.