## 1. Introduction




The term "ecology" was coined by the German zoologist,
Ernst Haeckel, in 1866 to describe the "economies" of
living
 forms.[1]
 The theoretical practice of ecology consists, by and
large, of the construction of models of the interaction of living
systems with their environment (including other living
 systems).[2]
 These models are then tested in the laboratory and the
field. (Field-work in ecology also consists of data collection that
need not be inspired by any theory.)




Theory in ecology consists of the heuristics--or
principles--used to construct models. Unlike evolutionary theory,
ecology has no generally accepted global principles such as Mendel's
(and other) rules of genetic
 inheritance.[3]
 Contemporary ecology consists of a patchwork of sub-disciplines
including population ecology, community ecology, conservation ecology,
ecosystem ecology, metapopulation ecology, metacommunity ecology,
spatial ecology, landscape ecology, physiological ecology,
evolutionary ecology, functional ecology, and behavioral ecology. What
is common to all these fields is the view that: (i) different biota
interact in ways that can be described with sufficient precision and
generality to permit their scientific study; and (ii) ecological
interactions set the stage for evolution to occur primarily because
they provide the external component of an entity's fitness. The latter
aspect makes ecology a central part of
 biology.[4]
 As van Valen once put it: "evolution is the control of
development by
 ecology."[5]
 However, the creation of a unified theoretical framework for
evolution and ecology remains the task for the future and will be of
no further concern in this entry.




Turning to the philosophy of ecology, beyond those interpretive and
conceptual problems that arise because of the evolutionary context,
ecology also presents interpretive and philosophical problems from its
own unique conceptual structure and experimental practices. In this
entry, attention will largely be restricted to population, community,
and ecosystem ecology since these have been the focus of the little
philosophical attention that has so far been afforded to ecology;
there will also be some limited treatment of spatial ecology (in
 Section 5.2).
 However, because ecology has not received the degree of professional
philosophical attention it deserves--while popular
"ecological philosophies" abound--this entry
concentrates more on foundational and interpretive issues raised by
the science of ecology rather than only on what philosophers have
written about the subject.




This entry treats experimental and theoretical work simultaneously
with a bias in favor of those theoretical results that are
unambiguously testable. It was once commonplace for even ecologists to
accuse ecological theory of
 untestability.[6]
 This criticism was not completely fair even in the past; moreover,
both experimental and theoretical developments during the last few
decades have removed much of its force. Instead, what ecology poses is
a much more philosophically intriguing set of seven problems:



> 
> 
> 
> |  |  |
> | --- | --- |
> | i.  | the *formalization indeterminacy* problem: apparently
> informally clear hypotheses can be translated into radically different
> formal counterparts. If one of these formal models turns out to be
> empirically incorrect, there is the danger that the original informal
> hypothesis will be rejected even though some other formalization may
> have saved the phenomena; |
> | ii.  | the *interpretation indeterminacy* problem: parameters in
> well-confirmed models can be interpreted in mutually exclusive
> ways; |
> | iii.  | the *structural uncertainty* problem: differences in
> theoretical assumptions and techniques make enormous predictive
> differences;[7] |
> | iv.  | *demarcation ambiguity* problem: the classification of
> models presents inordinate difficulties because the boundaries
> demarcating different ecological sub-disciplines is often unclear.
> Obviously, this problem is more a concern of philosophers (and others
> interpreting ecology) rather than of ecologists. |
> 
> 
> 



 Some typical examples of all these largely theoretical problems will
be pointed out later in this entry. Fieldwork, on which all empirically
sound ecological theory should presumably be based, poses an additional
problem:



> 
> 
> 
> |  |  |
> | --- | --- |
> | v.  | the *partial observability* problem: even the most basic
> parameters are often difficult to estimate accurately. One consequence
> is that many ecological models are often difficult to test in the
> field. |
> 
> 
> 




Finally, many philosophers and some ecologists have also argued that
ecological experimentation is confronted with two additional
problems:



> 
> 
> 
> |  |  |
> | --- | --- |
> | vi.  | the *complexity* problem: ecological systems are supposed
> to be not merely complicated, they are "complex." While
> much has been written about an alleged new science of complexity in
> recent years, no compelling operational distinction has yet been
> presented to distinguish complexity from complicatedness. In general,
> complex systems are supposed to exhibit "emergent"
> properties, that is those that, in some sense or other, resist
> reductionist explanation. The question of reductionism in ecology will
> be briefly discussed in this entry. Beyond that, no stance will be
> taken on the complexity versus complicatedness
>  issue;[8] |
> | vii.  | the *uniqueness* problem: ecological systems are
> supposed to be unique both because they are contingent historical
> entities, and structurally because of their complexity. By itself being
> a historical entity does not present any problem for scientific study:
> planets and galaxies are historical entities successfully studied by
> astronomers. However, ecological systems are supposed to have histories
> that result in highly contingent structures--part of their
> complexity--that make them special. |
> 
> 
> 




The last two problems often preclude the use of laboratory experiments
and model systems as a guide to what should happen in the
field. Moreover, according to skeptics, they are an impediment to the
replication of field experiments: the extent to which this is a
serious obstacle to ecological experimentation remains
controversial. These two problems will lurk in the background of all
the discussions here.




There are also many other general philosophical problems about science
that also occur in the ecological context, for instance, the role of
idealizations, models, and so on. But ecology does not seem to make
any unique contribution to these problems--they will be ignored
here in the interest of focusing on ecology *per se*. The aim
of this entry is to describe--in broad terms--the type of
philosophical questions raised by different areas of ecology so as to
encourage further philosophical work. No general conclusions will be
drawn from the various cases because none seem feasible at
present.


## 2. Population Ecology[9]




The golden age of theoretical ecology (1920-1940)--to borrow
the title of a book edited by Scudo and Ziegler--consisted
primarily of population
 ecology.[10]
 The next generation saw a shift of theoretical interest to community
ecology. In recent years, interest has reverted to population ecology,
sometimes in the form of metapopulation models (consisting of a set of
populations with migration between them). Models in population
ecology are based on representing an ecological system as the set of
populations (of the same or different species) it consists of. Each
population, in turn, consists of potentially interacting individuals
of a species. Populations may be characterized by their *state*
variables (parameters representing properties of the population as a
whole, for instance, size, density, growth-rate, *etc*.) or by
*individual* variables, that is, the properties of the
individuals in them (for instance, individual fecundity, interactions,
 *etc*.).[11]
 Classical population ecology was restricted to the study of
state-based models primarily because of the requirement that models be
tractable, so as to permit predictions and
 explanations.[12]
 State-based models will be the focus of this section;
individual-based models will be discussed in
 Section 5.1.
 Classical population ecology is the part of ecology that is
theoretically the most developed. The central issue of interest in
population ecology is the change in the size of populations (their
"abundances") over time.




Population ecology considers both deterministic and stochastic
models. Much of philosophical writing on population ecology has been
restricted to deterministic population models and this relatively large
body of work will only be very briefly summarized. More attention will
be paid to stochastic models which raise much more interesting
philosophical issues that have not been adequately explored.


### 2.1 Deterministic Models




If population sizes are large, they can be studied using deterministic
models, that is, fluctuations in populations sizes due to chance
factors (such as accidental births and deaths) can be ignored.
Usually a model considers members of a single or a very few
interacting species, for instance, a few predator and a prey
 species.[13]
 A typical result, based on the Lotka-Volterra (coupled differential
equations) model, is that predator-prey interactions lead to
population cycles, with the predator population cycle temporally
tracking the prey population cycle. The explanation of this phenomenon
is straightforward: as prey populations increase, the increased
availability of resources allows a rise in predator populations a
little later in time. But the increase of predators leads to an
increase of prey consumption and, consequently, a decrease in prey
populations. But, now, the lack of resources leads to a decline of
predator populations. As predator populations decline, prey
populations increase initiating the cycle once again. See
Figure 1:



> 
> ![Figure 1](figure1.jpg)
>   
> 
> **Figure 1: Predator-Prey Population Cycles**:   
> 
>  The model is due to Volterra ([1927] 1978, pp. 80-100, the diagram is
> from p. 100). There are two species, a predator species with a
> population, *N*2, which only feeds on a single prey
> species with population, *N*1. The model incorporates
> demographic stochasticity which, nevertheless, does not stamp out the
> basic cyclic pattern. (*t* is a measure of time.)
>  



 The Lotka-Volterra model mathematically predicts these cycles. As
such, it exemplifies the explanatory ideal of ecology: not only is
there a predictively accurate quantitative model, but the mechanisms
incorporated in the model have a perspicuous biological
interpretation. Unfortunately, in ecology, because of the
formalization and interpretation indeterminacy problems, the last
condition is rarely satisfied.




For the simpler case of single species, two standard models are that
of exponential and logistic growth. The exponential growth model is
supposed to capture the behavior of a population when there is no
resource limitation; the logistic growth model is one of the simplest
ways to try to capture the self-regulation of population sizes when
there is such a
 limitation.[14]
See Box 1.1 and 1.2.



**Box 1.1 Exponential Growth Model**  

Let a population consist of *n* individuals
at time *t*. Suppose that, in an infinitesimal time
interval between *t* and *t*+*dt*,
a fraction *b*/*n* of individuals give 
birth and a fraction *d*/*n* die. Let the
change in the size of the population be *dn*. Then

 
> 
> *dn*/*dt* =
>  (*b* [?] *d*).
>  


Let *r* = *b* [?] *d*. Then the
population dynamics is described by the growth equation:



> 
> *dn*/*dt* = *m*.
>  


This is the exponential growth model. It
assumes that no resource limitation constrains the "intrinsic
growth rate", *r*. It can be solved to give:



> 
> *n*(*t*) =
>  *n*0*e**rt*,
>  


where *n*0 is the size of the population
at *t*0. 



  



**Box 1.2 Logistic Growth Model**  

One way to modify the exponential growth model to
incorporate resource limitation is to replace the growth
equation of the exponential
model by that of the logistic growth model:

 
> 
> *dn*/*dt* = *m*(1 [?] *n*/*K*),
>  


where *K* is called the "carrying capacity" of 
the environment; this parameter is supposed to incorporate
how resource limitation affects population growth by regulating
it. When *n* = *K*, the growth rate,



> 
> *dn*/*dt* = 0
>  


and the population does not grow any further. Moreover, 
when there is no resource limitation, that is *K*
- [?], this model reduces to the exponential
growth model. Figure 2b shows how a population 
governed by the logistic equation grows in size.
At the level of individual behavior, this model
does not have the kind of justification that the exponential
growth model does in the sense that the 
logistic equation cannot be plausibly derived from the
properties of individuals. In this sense it is a purely
"phenomenological" model.




 The exponential growth model appeals to only one essentially
ecological parameter, the intrinsic growth rate (*r*) of a
population, interpreted as the rate at which the population would grow
if there were no external factor limiting growth; the logistic model
also appeals to the carrying capacity (*K*), interpreted as the
maximum size of the population that can persist in a given
environment. See Figures 2a and 2b:



> 
> 
> 
> ![Geometric increase of number of individuals over time](figure2a.jpg)  
> 
>  (a)
> 
> 
> 
> ![](figure2b.jpg)
>   
> 
>  (b)
> 
> 
> 
> **Figures 2a and 2b: The Logistic Growth Model**
>   
> 
>  The figure on the top (from Gause [1934], p. 35) shows theoretical
> curves. "Geometric increase" represents the exponential
> growth model discussed in the text; "saturating population"
> refers to the carrying capacity (see
>  Box 2).
>  The figure on the bottom (from Gause [1934], p. 36) shows
> an example of an empirical growth curve obtained in the laboratory. If
> the curve is fitted to a logistic curve (to which it shows similarity),
> then *K* = 375 is the estimated carrying capacity.
> 
> 
> 



 In general, biological experience suggests that all populations
regulate their sizes, that is, they show self-regulation. Theoretical
exploration of models has made it clear that a wide variety of
mechanisms can lead to such self-regulation but it is usually unclear
which models are more plausible than others thanks to the typical
formalization indeterminacy of the field. Moreover, the precise
mechanisms that are playing regulative roles in individual cases are
often very hard to determine in the field, a classic case of partial
observability. Even parameters such as the intrinsic growth rate and
carrying capacity are unusually difficult to estimate precisely.




The last mentioned difficulties are perhaps most famously illustrated
by the 10-year cycle of snowshoe hares, muskrats and their predators
in the North American boreal forests and, especially, the 4-year cycle
of lemmings and, possibly, other microtines in the arctic tundra of
Eurasia and North America. In spite of almost seventy-five years of
continuous research on these well-documented cycles the mechanisms
driving them remain unresolved. Models producing such cycles abound,
but the structural uncertainty of most of these models, coupled with
partial observability of many of the parameters in the field have
precluded resolution of the
 debate.[15]




The models discussed so far are continuous-time models, that is, the
temporal or dynamic parameter is assumed to be a continuous variable.
However, discrete-time models have also been used to study population
processes. A discrete analog of the logistic growth model was one of
the first systems in which chaotic dynamic phenomena were
 discovered.[16]
 Over the years there has been considerable debate over the question
whether ecological systems with chaotic dynamics exist in nature; the
current consensus is that they have not yet been
 found.[17]


### 2.2 Stochastic Models




If population sizes are small, then models should be stochastic: the
effects of fluctuations due of population size must be explicitly
analyzed. Stochastic models in ecology are among the most
mathematically complex models in science. Nevertheless they have begun
to be systematically studied because of their relevance to biological
conservation--see the entry on 
 conservation biology.
 They also raise philosophically interesting questions because they
underscore the extent to which the nature of randomness and
uncertainty remains poorly explored in biological
 contexts.[18]




What has, by and large, become the standard classification of
stochasticity goes back to a 1978 dissertation by Shaffer. The context
of that dissertation provides a striking exemplar of the social
determination of science. The United States National Forest Management
Act of 1976 required the Forest Service to "provide for
diversity of plant and animal communities based on the suitability and
capability of the specific land
 area."[19]
 In 1979 the planning regulations developed to implement this
provision required the Forest Service to "maintain viable
populations of existing native and desired non-native vertebrate
species in the planning
 area."[20]
 A viable population was defined as "one which has the estimated
numbers and distribution of reproductive individuals to insure its
continued existence in the planning area." For large
populations, falling within the domain of deterministic models,
establishing viability is relatively trivial: all that must be ensured
is that, on the average, a population is not declining in size. For
small populations, even if it is increasing in size on the average, a
chance fluctuation can result in extinction. Stochastic models are
necessary to predict parameters such as the probability of extinction
within a specified time period or the expected time to extinction.




In his dissertation, Shaffer attempted such an analysis for the grizzly
bears (*Ursus arctos*) of Yellowstone which were believed to
face the prospect of stochastic extinction. Shaffer distinguished four
sources of uncertainty that can contribute to random extinction:


1. *demographic stochasticity* which arises from the chance
events in the survival and reproductive success of a finite number of
individuals...
2. *environmental* *stochasticity* due to temporal
variation of birth and death rates, carrying capacity, and the
population of competitors, predators, parasites, and
diseases...
3. *natural catastrophes* such as floods, fires, droughts,
etc....
4. *genetic stochasticity* resulting from changes in gene
frequencies due to founder effect, random fixation,
or
 inbreeding."[21]




Shaffer went on to argue that all these factors increase in importance
as the population size decreases--a claim that will be questioned
below--and, therefore, that their effects are hard to
distinguish. Finally, he defined a minimum viable population (MVP):
"A minimum viable population for any given species in any given
habitat is the smallest population having at least a 95% chance of
remaining extant for 100 years despite the foreseeable effects of
demographic, environmental, and genetic stochasticity, and natural
 catastrophes."[22]
 Both numbers (95% and 100 years) are conventional, and to be
determined by social choice, rather than by biological factors, a
point that Shaffer explicitly recognized.




In the 1980s, techniques to determine MVPs came to be called
"population viability analysis" and the enthusiasm for the
new framework was captured in the much-worn slogan: "MVP is the
product, PVA the
 process."[23]
 By the late 1980s, however, it became clear that the concept of a MVP
was at best of very limited use. Leaving aside the conventional
elements of the definition given above, even for the same species,
populations in marginally different habitat patches often show highly
variable demographic trends resulting in highly variable MVP estimates
for them, with each estimate depending critically on the local
 context.[24]
 Moreover, as will be illustrated below for stochastic population
models in general, the determination of MVPs suffers from irremediable
structural uncertainty. This should not come as a surprise: what would
have been more surprising is if legislative fiat had identified a
scientifically valuable parameter. After the demise of the concept of
the MVP, PVA began to be performed largely to estimate other
parameters, especially the expected time to extinction of a population
the estimation of which does not require any conventional
choices--see the entry on 
 conservation biology.




Within the context of PVA, Shaffer's classification--though
usually with "random catastrophe" replacing "natural
catastrophe"--became canonical within both ecology and
conservation
 biology.[25]
 However, the philosophical question remains as to whether it makes
sense--classification, as many philosophers have pointed out, is
not innocent of substantive theoretical assumptions. The first point
to note is that genetic stochasticity is not even the same type of
mechanism as the other three: its presence makes Shaffer's
classification oddly heterogeneous. The reason for this is that
genetic stochasticity is a consequence of demographic stochasticity:
in small populations, a particular allele may reach fixation purely by
chance reproductive events. It is even possible that stochasticity
increases the rate at which a beneficial allele may go to fixation in
a small population provided that the initial frequency of that allele
is already high.




Leaving genetic stochasticity aside, do the other three categories
provide a good classification of stochasticity, or is the
classification more like that of animals in Borges' notorious Chinese
 encyclopedia?[26]
 For any classification to be a good one, it must, at the very least
satisfy three criteria: (i) it must organize phenomena into a
relevantly homogeneous class of categories. Relevance is determined
contextually by the possibility of there being a coherent account of
how those categories came to be defined. Including genetic
stochasticity in the classification leads to a lack of such
coherence--hence, its exclusion above; (ii) the categories should
be jointly exhaustive, able to subsume all cases of the relevant
phenomena; and (iii) the categories should be mutually exclusive, that
is, no phenomenon should be subsumed under more than one category.




It is the third criterion that is often called into question by
Shaffer's classification of stochasticity primarily because of
formalization indeterminacy. Consider some small reptile that fails to
breed because a flood creates a barrier across its habitat that it
cannot cross and there are no available mates on its side of the
barrier. Is this environmental or demographic stochasticity? On the
one hand, it is obviously environmental because floods are precisely
the type of mechanism by which environmental stochasticity is
expressed. On the other hand, it is equally obviously demographic
because the failure to reproduce is due to the chance unavailability
of a mate on the appropriate side of the barrier. Ultimately, as will
be discussed below in some detail, whether this is a case of
demographic or environmental stochasticity depends on how it is
modeled. There is an important philosophical lesson here: especially
when a new discipline is being formed, the structure of the
phenomena--how they are distinguished and classified--are in
part determined by the models used to represent them. Consequently,
classification is not theoretically innocent. This is as true in
ecology as in any other scientific context.




Thus, as a prelude to modeling, Lande *et al*. argue that
demographic stochasticity "refers to chance events of individual
mortality and reproduction, which are usually conceived of as being
independent among individuals" whereas environmental
stochasticity "refers to temporal fluctuations in the
probability of mortality and the reproductive rate of all individuals
in a population in the same or similar fashion. The impact of
environmental stochasticity is roughly the same for small and large
 populations."[27]
 This is further elaborated: "Random variation in the expected
fitness that is independent of population density constitutes
environmental stochasticity. Random variation in individual fitness,
coupled with sampling effects in a finite population, produces
demographic
 stochasticity."[28]
 Nevertheless, environmental fluctuations or even random catastrophes
affect the size of a population only insofar as they affect
reproduction and death rates, that is, by creating demographic
fluctuations; at least in this sense, the second and third categories
of stochasticity are not conceptually independent of the first.
Moreover, Lande and others regard random catastrophes as extreme cases
of environmental stochasticity. Consequently, it requires explicit
mathematical models in which these distinctions are made exact through
formal
 definitions.[29]
 Usually, models of demographic stochasticity are distinguished from
models of environmental stochasticity using as a criterion whether the
stochastic factor explicitly depends on the population size as a
parameter. If it does, the model in question is one of demographic
stochasticity; if it does not, it is one of environmental
stochasticity. This choice captures the intuition mentioned earlier
that the effect of the former depends on the population size whereas
the effect of the latter does not.




The mathematical analysis of these models is non-trivial. The most
general and uncontroversial theoretical result to date is that
progressively larger populations are required for safety in the face
of demographic, environmental, and random catastrophic stochasticity.
Moreover, because of the structural uncertainly of these models,
apparently slight differences in assumptions and techniques routinely
lead to widely divergent predictions. This can be illustrated using
the well-studied example of the Yellowstone grizzlies. In 1994 Foley
constructed a model for this population incorporating environmental
stochasticity alone and depending on the intrinsic growth rate of the
population and the carrying capacity of the
 environment.[30]
 The model's prediction augured well for the grizzlies: with a
reasonable value for the carrying capacity and the measured value for
the intrinsic growth rate, the expected time to extinction was about
12,000 years. In 1997 Foley constructed another model incorporating
both demographic and environmental stochasticity but with the option
of setting either part equal to
 0.[31]
 When this model is solved with the demographic stochasticity equal to
0 (a case not analyzed by Foley), it should give the same result as
the 1994 model. It does not. It predicts a much lower expected time to
 extinction.[32]


## 3. Community Ecology




Community ecology consists of models of interacting species, forming
an ecological "community," in which each species is
treated as a unit. The appropriate definition of
"community" has been widely debated among ecologists and
philosophers; what is being given here is an interactive
 definition.[33]
 Alternative options include defining community by mere geographical
association of species at one extreme, or by requiring a good deal of
structure in the interactions at the other, making the community
analogous to an organism. The interactive definition given above is
attractive for two reasons: (a) mere association leaves little of
theoretical or practical interest to study, while requiring some
specified elevated levels of interaction introduces an unnecessary
arbitrariness in a definition of community; and (b) the former would
make any association of species a
 community[34]
 whereas the latter would typically introduce so much structure that
virtually no association would constitute a community.




Community models can be conveniently represented as
loop,
 diagrams[35]
 generalized graphs that have each species as a vertex and edges
connecting these vertices when the species interact. The edges
indicate whether the relevant species benefit or are harmed by the
interaction, that is, whether they tend to increase or decrease in
abundance, by an interaction. See Figure 3:



> 
> 
> ![Figure 3](figure3.jpg)
>   
> 
> **Figure 3: Structure of an Ecological Community**
>   
> 
>  This is the loop diagram of a community of three species (from Diamond
> [1975a], p. 435). Species *X*1 and
> *X*2 both prey upon the resource species
> *X*3. There is resource coupling and competitive
> exclusion. It is assumed that all three species are self-regulating.
> -: positive interaction; 
>  ![circ-arrow](circ-arrow.jpg): negative interaction. The
> coefficients measure the strength of the interactions.
>  



 As with population ecology, what is of most interest is are the
changes in a community over time. This brings us to one of the most
interesting--and one of the most vexed--questions of
ecology: the relationship between diversity and stability. A deeply
rooted intuition among ecologists has been that diversity begets
stability. If this claim is true, it has significant consequences for
biodiversity conservation--see
 conservation biology.[36]




What confuses this question from the very beginning, is the
multiplicity of possible definitions of "diversity" and
"stability." There are probably no better instances of
formalization indeterminacy in any scientific context. For instance, a
reasonable first attempt to define diversity would be to equate the
diversity of a community to the number of species in it, that is, its
species "richness." The trouble is that there is ample
reason to doubt that richness captures all that is relevant about
diversity, whether or not we are interested in only its relationship
to stability. Consider two communities, the first consisting of 50%
species *A* and 50% species *B*, and a second consisting
of 99.9 % species *A* and 0.1% species *B*. Both
communities have the same richness because they both have two species;
however, there is a clear sense in which the first is more
diverse--or less homogeneous--than the second. Moreover, the
difference is likely to be relevant. If diversity does beget stability
in these communities, then that stability must be a result some
interaction between the two species. If species *B* comprises
only 0.1% of the community, the scope for such interaction is
typically much less than if it comprises
 50%.[37]
 Diversity must mean more than richness. There
have been several attempts to define and quantify diversity beyond
richness; one of them is described in Box 2.




**Box 2. Measures of Diversity**  

Ecologists often distinguish between three concepts
of biodiversity, usually using species as the appropriate unit to
measure diversity: (i) "a-diversity", the diversity
within a community/place (MacArthur 1965); (ii)
"b-diversity", the diversity between
communities/places (Whittaker 1972, 1975); and (iii)
"g-diversity", the diversity between regions, that
is, b-diversity on a larger spatial scale (Whittaker 1972).

 Though many measures of a-diversity have been proposed over
the years, MacArthur's (1965) proposal to use the Shannon measure of
information content in a communication process (Shannon 1948) has
remained the most popular (though not universally accepted). 


According to this measure, the a-diversity of a community
with *n* species is given by



> 
> 
> 
> |  |  |  |  |
> | --- | --- | --- | --- |
> | a =  | [?] | *n*
> [?] 
> *i*=1 | *p**i* ln *p**i* |
> 
> 
> 


where *p**i* is the frequency of
the *i*-th species. This is a measure of the diversity of a
community in the same way that the Shannon measure of information
content is a measure of the variety in a signal. 


Turning to the two communities discussed in the text, a simple
calculation shows that the diversity of the first is given by a
= 0.693, while the diversity of the second is given by a =
0.008, verifying the intuition that the first is more diverse than the
second.




 Unfortunately, though, there has been little success in tying these
concepts to theoretical rules or even empirical
 generalizations.[38]
 (For a discussion of the related concept of biodiversity, see
the entry on 
 biodiversity.)




Stability turns out to be even more difficult to
 define.[39]
 At one extreme, stability can be defined to require that a community
be truly in equilibrium: it does not change in either its composition
(the abundances of every component)or in the interactions among these
components. At the practical level, this definition faces the problem
of vacuous scope: almost no natural community satisfies such a strict
requirement of equilibrium. Moreover, almost every community
experiences significant disturbances. With this in mind, stability has
been variously explicated using a system's response to disturbances or
its tendency not to change beyond specified limits even in the absence
of disturbance. Boxes 3 and 4 (see below) list some of the definitions of stability that
have been in vogue and how they may be measured in the
 field.[40]




How do any of these measures of stability relate to diversity? The
only honest answer is that no one is sure. If diversity is interpreted
as richness, traditionally, it was commonly assumed that diversity is
positively correlated with at least persistence. However, there was
never much hard evidence supporting this assumption. If stability is
interpreted as a return to equilibrium, mathematical models that
should answer questions about stability are easy to construct but hard
to analyze unless the system is already close to equilibrium. This is
called local stability analysis. The most systematic analyses
performed so far give no straightforward positive
 correlation.[48]
 It was once believed that natural ecosystems are usually at
equilibrium (the "balance of
 nature").[49]
 But ample empirical data now suggests that this assumption is almost
never correct: natural ecosystems are usually far from
 equilibrium.[50]
 Moreover, if natural selection between species occurs during the
transition to equilibrium, equilibrium communities will be less rich
than those that are yet to reach equilibrium. On short time scales
(short enough to make speciation unlikely), selection between species
that utilize the same resources (that is, they occupy the same
"niche") will lead to the exclusion of the less fit by the
more fit through "competitive
 exclusion."[51]
 The eventual equilibrium community, one in which selection would no
longer be acting, the (controversially) so-called "climax
community," is necessarily less rich than those that temporally
preceded it.


**Box 3. Perturbation-Based Categories**   



|  |  |
| --- | --- |
| **Category** | **Measure** |
| **Local Stability** -
probability of a return to a reference state or dynamic after a
change (not caused by a perturbation) in the value of a system's
state
 variables.[41] | Measured in the same parameters as the state variables. |
| **Resilience** -
rate at which a system returns to a reference state or dynamic
after a
 perturbation.[42] | The inverse of the time taken for the effects of a perturbation
(e. g., of species' abundances or densities) to decay relative to the
initial size. |
| **Resistance** -
inverse of the magnitude of the change in a system, relative to a
reference state or dynamic after a
 perturbation.[43] | 1. Inverse of the change of species' densities or abundances
relative to the original state.
 2. Change of species composition relative to the original
composition. |
| **Perturbation Tolerance / Domain of Attraction** -
size of the perturbation a system can sustain and return to a
reference state or dynamic (irrespective of time
 taken).[44] | Perturbation size measured in natural units (perturbation may be
biotic or abiotic). |


  


**Box 4. Perturbation-Independent Categories**   



|  |  |
| --- | --- |
| **Category** | **Measure** |
| **Constancy**.
Inverse of the variability of a system (community or
 population).[45] | The inverse of the size of fluctuations of some parameter of the
system such as species richness, size or biomass abundance. |
| **Persistence**.
Ability of the system to continue in a reference state
or
 dynamic.[46] | 1. The time a system sustains specified minimum population levels,
e.g., non-extinction of a proportion of its species.
 
 2. The time a system will sustain specified species compositions. |
| **Reliability**.
Probability that a system (community or population) will
continue
 "functioning"[47] | Measured in terms of how faithfully and efficiently a system
processes energy, materials, and engages in other biogeochemical
activities. |




The traditional assumption of a general positive correlation between
diversity (as richness) and stability has been seriously challenged on
both theoretical and empirical grounds since the
 1970s.[52]
 However, more recently, Tilman has suggested an empirical connection
between richness and stability, interpreted as constancy, in grassland
 habitats.[53]
 The scope of this generalization, even whether it can be replicated
for other grasslands than those that Tilman studied, remains to be
investigated. Meanwhile, Pfisterer and Schmid have produced equally
compelling empirical evidence that richness is inversely correlated
with stability, interpreted as resilience and
 resistance.[54]
 Much remains to be found out. All that is certain is that McCann's
confident 2000 verdict in favor of a positive diversity-stability
relationship was
 premature.[55]
 Note, finally, that virtually no theoretical or practical exploration
of this question has used concepts of diversity other than
richness. At least at the theoretical level, this remains an open
field for philosophers. (Clear formal results would not go unnoticed
by ecologists.)




Within community ecology, philosophers have lately paid considerable
attention to the theory of island biogeography and the controversies
surrounding its relevance for the design of networks of biological
 reserves.[56]
 The basis for this theory is the species-area relation: larger areas
of the same habitat type usually contain more species than smaller
 ones.[57]
 Thus there is a monotonic relationship between species richness and
area. But, what is the form of this relationship? Moreover, what is
the mechanism responsible for it? In spite of sporadic work over
almost an entire century, these remain open questions. Perhaps the
most popular answer to the first question, but one that gives no hint
of the operative mechanism, is a power law going back to
 Arrhenius:[58]
*S* = *cAz*, where *S* is the
number of species, *A* the area, and *c* and *z*
constants. This power law represents what is often called the
"species-area curve." Turning to the question of
mechanisms, traditionally, the species-area relation was attributed to
environmental heterogeneity. Larger areas were presumed to have
greater habitat heterogeneity and could, therefore, host a larger
number of species each with its own specific needs. In recent years
the relation is more often attributed to the belief that larger areas
can support larger populations of any
 species.[59]
 Thus fewer populations are likely to become extinct in a larger area
than a smaller one in any specified time interval. Consequently, on
the average, more species are likely to be present in larger areas
than smaller ones even if both started with the same species
richness.




Whether the species-area curve (rather than the mere qualitative
relation) has any empirical support remains a matter of
 contention.[60]
 In the 1950s, Preston was a strong advocate of the power law model
which he believed to be the result of a dynamic equilibrium of the
exchange of species between isolated habitat
 patches.[61]
 (The same idea had been worked out in some detail much earlier by
Munroe but did not receive any
 attention.)[62]
 Preston's work was extended by MacArthur and Wilson to construct the
theory of island
 biogeography.[63]
 According to this theory, the number of species in islands with the
same habitat (at the same latitude) depends only on the size of the
island and its isolation. There is a dynamic equilibrium in the sense
that this number does not change over time though there is a turnover
of species which changes the composition of the
 community.[64]
 The equilibrium is supposed to be a result of a balance between
immigration and extinction. The immigration rate varies inversely with
the degree of isolation while the extinction rate decreases with
area. Thus, this theory incorporates the second mechanism for the
species-area relation mentioned in the last paragraph. While some
initial experimental evidence seemed to support the theory, by the
mid-1970s its status had become
 controversial.[65]




Nevertheless, in the 1970s, island biogeography began to be viewed as
a model for biological reserves which, by being surrounded by
anthropogenically transformed lands, were supposed to be similar to
islands--see the entry on
 conservation biology.
 The initially prevalent view, based on island biogeography theory,
was that reserves should be as large as
 possible.[66]
 In particular, one conclusion drawn from island biogeography theory
was that "[i]n cases where one large area is infeasible, it must
be realized that several smaller ones, adding up to the same total
area as the single large one, are not biogeographically equal to it:
they will tend to support a smaller species
 total."[67]
 Though this conclusion was incorporated into the design of the World
Conservation Strategy of the International Union for the Conservation
of Nature, there was almost no data that ever supported
 it.[68]
 It was also challenged by Simberloff and Abele on both
theoretical and empirical
 grounds.[69]
 Among other things they pointed out that several small reserves may
increase the probability of the survival of species in the face of
environmental stochasticity, for instance, random catastrophes such as
outbreaks of infectious disease. This objection sparked the SLOSS
(Single Large or Several Small) debate about the design of biological
reserve networks. The SLOSS debate dominated discussions of reserve
network design for about a decade. Meanwhile the species-area curve
also began to generate serious skepticism. Soule *et
al*. predicted in 1979 from a model based on the species-area
curve that the Serengeti National Park of Tanzania will lose 50% of
its large mammals (15 ungulate species) in 250
 years.[70]
 However, once Western and Ssemakula incorporated habitat diversity
data in 1981, it appeared that only 1 such species will go
 extinct.[71]
 There are many other such examples and it is hard not to sympathize
with Zimmerman and Bierregard who observe that, besides the ecological
truism that species richness increases with area, there is little of
value in the species-area curve (and the theory of island
 biogeography).[72]
 In the context of biodiversity conservation, the relevance of the
species-area curve, and island biogeography, has never been
uncontroversially accepted. Important early criticism of the use of
island biogeography theory for reserve network design came from
Margules and several collaborators in
 1982.[73]
 They pointed out that the theory was yet to be empirically
established in the field, that biological reserves were not very
similar to islands because landscapes between reserves were not
completely uninhabitable by the species in the reserves (unlike the
case of oceans separating islands), that habitats are largely
heterogeneous rather than homogeneous (as assumed in the theory), and
that species richness should not be the only criterion used to select
reserves. By 1986, it became clear that there would be no winner in
the SLOSS debate; since then there has been no unequivocal role for
island biogeography theory to play in the design of biodiversity
reserve
 networks.[74]
 (For more on these issues, see the entry on 
 conservation biology.)


## 4. Ecosystem Ecology




The term "ecosystem" was coined in 1935 by Tansley who
defined it as "the whole *system* (in the sense of
physics) including not only the organism-complex [that is, the
community], but also the whole complex of physical factors forming
what we call the environment of the biome--the habitat factors in
the widest
 sense."[75]
 Tansley went on to argue that ecosystems "are the basic units
of nature on the face of the earth." For Tansley, using the term
"ecosystem" implied a physical description of a community
in its habitat. Even though that perspective still illuminates
ecosystem studies (see below), it is no longer a necessary or even
common connotation of the term "ecosystem." However,
independent of the use of "ecosystem," ecosystem ecology,
in contrast to other ecological sub-disciplines, and in continuity
with its history, does retain an emphasis on physical processes.
Should ecosystem ecology, then, be regarded as an instance of the
unification of the physical and biological sciences? There has been so
little philosophical attention to ecology that this question does not
appear ever to have been broached.




The introduction and rapidly growing popularity of the term
"ecosystem," especially during the late 1950s and 1960s,
was marked by two major cognitive and one sociological shift in the
practice of ecology: (a) coming at the end of the so-called golden age
of theoretical population ecology of the late 1920s and 1930s (see
 Section 2),
 turning to ecosystems helped shift emphasis from
populations with interacting individuals to much larger and more
inclusive systems. In this sense it was a deliberate
anti-"reductionist"
 move.[76]
 Ecosystem enthusiasts follow a long holistic tradition in natural
history that tends to deify complexity and deny the possibility of
explaining wholes in terms of their
 parts.[77]
 "Systems thinking" was supposed to replace
reductionism, the decomposition of wholes into parts for the sake of
analysis (see
 Section 5);
 (b) a second cognitive shift is that ecosystem studies
involve models based at least partly on
non-biological
 variables.[78]
 For instance, instead of tracking individuals or even species in
communities, models may track energy or matter flow in food webs as a
whole; and (c) at the sociological level, the expansion of ecosystem
studies led to what one historian has called the invention of
"big biology" in the 1960s, chiefly in the
 US.[79]
 These studies, for instance the massive Hubbard Brook
Ecosystem
 Study,[80]
 required more than just many biologists working together. They also
demanded that other specialists, including geochemists and soil
scientists, be brought in so that all the relevant physical parameters
of ecosystems, besides the biological ones, could be tracked
simultaneously. This study constituted the biologists' attempt to
engage in publicly-funded Big Science, initiated by the physicists
during the Manhattan Project, and subsequently profitably exploited by
social scientists since the 1950s.




Until the last decade, some seventy years after the introduction of
the term "ecosystem" and forty years after a veritable
explosion of ecosystem studies, it was less than clear what important
new insights this disciplinary move produced. The trouble was that, at
this level of analysis, very few general claims could be sustained.
Those that could--for instance, that Sun is ultimately the source
of all energy in biological systems or that primary producers have to
contain chlorophyll or some other such molecule--were usually
trivial and well-known long before the initiation of systematic
large-scale ecosystem studies in the 1960s. Usually ecosystem studies
produced detailed analyses of nutritional or climatic requirements of
particular communities. But the details of nutritional requirements
were either so general as to be almost irrelevant, or so specific that
they were rarely transportable from one ecosystem to another. Almost
all of what is known about climatic requirements of vegetation types
(and other communities) was known to biogeographers long before the
invention of ecosystem studies. The carbon and nitrogen cycles had
also been worked out long before the advent of ecosystem studies as an
organized discipline.




However, the physical characteristics of habitats do matter to
organisms living in them. Moreover, physical changes on a global
scale, for instance, climate change through global warming, have
serious long-term implications for
 biota.[81]
 The changes other than climate change include increasing
concentration of carbon-dioxide in the atmosphere and changes in the
biogeochemistry of the global nitrogen cycle, besides changes in land
cover and land
 use.[82]
 During the last decade, ecosystem studies and models have finally
matured to produce novel intellectual insights even about previously
well-characterized ecosystem processes such as the carbon and nitrogen
 cycles.[83]
 For instance, the effects of disturbance and fire on ecosystem
processes are now being seriously
 assessed.[84]




In one interesting analysis--one among many--Ryan has used a
complex model tying physiological processes to the physical
environment to suggest that increased temperature will make
maintenance respiration (which represents the physiological costs of
protein synthesis and replacement, membrane repair, and the
maintenance of ion gradients in cells) for plants more
 difficult.[85]
 This is important because the total plant respiration, including
maintenance respiration, is an important component of the carbon
balance in any ecosystem. Ryan's model is based on observed
statistical associations of the different parameters; the underlying
mechanisms resulting in the high sensitivity of maintenance
respiration to temperature change (as well as changes in many other
physical parameters such as carbon dioxide and protein concentrations)
remain unknown. Ryan's result is important because of the ongoing
climate change through global warming.




In another example, Aerts and Chapin provide a systematic review of
nutritional ecology of wild plants including nutrient-limited growth,
nutrient acquisition, use efficiency, and recycling through
 decomposition.[86]
 This review underscores the conclusion that plant growth in
terrestrial ecosystems is not only very often controlled by nitrogen
availability in the nutrients but that it is also often similarly
dependent on phosphorus availability.




What has made much of the new work possible is not only increased
experience with ecosystems but also significant technical innovation,
including the advent of high-speed microcomputers, satellite imagery,
and Geographic Information Systems (GIS) which will be discussed next
(in
 Section 5).
 The future of ecosystem ecology appears much more
secure today than it did a decade ago.


## 5. New Directions




There have been two recent developments in ecology which are of
general philosophical interest; moreover, they help mitigate the
problems of complexity and uniqueness noted in
 Section 1.
 Both developments were made possible by the astronomical
increase in the speed and ease of computation since the early
1980s.


### 5.1 Individual-Based Models




In the type of population ecology that was discussed earlier (in
 Section 2),
 populations were characterized by their *state* variables,
parameters such as size or density describing the population as a
whole and--with two exceptions--ignoring individual
differences. The exceptions are age and stage; the age or stage
structure of populations (the fraction of individuals in each age or
developmental stage class) is sometimes incorporated in the
traditional models of population ecology. Since about 1980, that
situation began to change when the so-called "individual-based
models" (IBMs), which incorporate individual differences, began
to be explored
 systematically.[87]
 IBMs represent a population as a collection of individuals with
variable properties such as size, growth-rate, biomass, and so on. The
interactions between individuals are incorporated into the
model. Since, because of their sheer complexity, such models are
typically impossible to study analytically, they are studied by
simulation on a computer. The wealth of detail that can be
incorporated into IBMs allows specific predictions to be made. Part of
the attraction of IBMs has been their relatively greater predictive
success compared to other types of ecological
 models.[88]




IBMs are particularly useful because they can also be spatially
explicit (see
 Section 5.2),
 that is, they can incorporate locational relationships between the
individuals being modeled. These models have even been used to assess
change on a global scale. For instance, forest models (which are among
the most successful IBMs) have been used to assess the result of
climate change on the atmosphere because of a potential breakdown of
the presumed balance between production and decomposition of
carbon-containing compounds. Such an extrapolation of scale relies on
sampling each of the terrestrial lifezones and constructing some IBMs
for all of them, and subsequently integrating the
 results.[89]
 The future will show how reliable this strategy is. IBMs have also
recently begun to be used for population viability analysis, tracking
the trajectory of each individual during its
 lifetime.[90]
 This use is likely to grow. In both the situations discussed here,
the main problem with the use of IBMs is the immense quantity of
reliable data that they require.




Within the context of population ecology, since the behavior of the
entire population is putatively being explained on the basis of the
properties of the individuals within them, using IBMs is,
philosophically, a reductionist strategy called "methodological
 individualism."[91]
 Here, "reductionism" means that models of large systems
should try to explain their behavior *entirely* in terms of the
properties of their parts (nowhere referring to intrinsically
 "systemic"[92]
 properties). More specifically, such a reductionism amounts to the
assumption that properties and interactions of individuals alone
suffice to explain all behavior at the level of populations (and
higher units): there is no need to refer to higher level or systemic
properties which cannot be defined in terms of individual properties
(for instance, the density of a population). Moreover, since
interactions between individuals of different species can also be
incorporated into these models, community-level properties can also
potentially be explained by IBMs. For instance, the structure of food
webs can potentially be explained by IBMs that take into account
habitat size and
 resources.[93]
 Thus, even community structure is potentially reducible
to individual interactions. In this sense, community ecology, like
population ecology, is also being reduced to
 IBMs.[94]
 In this way, IBMs are unifying at least these two subfields within
ecology. Demarcation ambiguity is not a problem for IBMs; rather, it
is a virtue. It remains surprising how little philosophical attention
IBMs have so far received. If they succeed, they will help end the
long and, at least arguably, sterile tradition of anti-reductionism
(or holism) in ecology.




Nevertheless, an important limitation of IBMs should not go unnoticed:
models of this type--that is, all models that have to be analyzed
by computer simulation--have the shortcoming that they often fail
to provide theoretical insight in the sense of identifying the
critical heuristics or principles that are responsible for a type of
behavior. Are the dynamical rules responsible for some behavior? Or
the structural constraints, such as the initial conditions? Or the
precise parameter values? To answer such questions--which is at
least part of what theoretical understanding consists
of--minimally requires the simulation of a large class of related
models, often hard to achieve in practice. It remains the case that
these questions can often easily be answered using traditional
mathematical models: an ounce of algebra may well be worth a ton of
computer simulation. Thus any defense of reductionism in ecology based
on IBMs must be very limited.


### 5.2 Spatial Ecology and Geographical Information Systems




What has perhaps the greatest potential for altering the shape of
ecology is the advent of Geographic Information Systems (GIS) which
enables the detailed spatial representation and rapid manipulation of
geographical data on computers. GIS came along at a time when
ecologists had already begun to explore the role of spatial structure
on the dynamics of populations, communities, and ecosystems. Within
spatial ecology these were represented as entities having spatial
relations with each other, besides the traditional ecological
relations defined by their interactions. However, before GIS,
tractable models required the idealization of uniform geometries (even
in IBMs). The advent of GIS allowed the replacement of this
idealization with more veridical spatial relations. Since philosophers
of science have so far paid little attention to the history or
implications of GIS technology, the discussion here will be somewhat
more detailed than the treatment of other aspects of ecology.




GIS originated in sparsely-populated Canada which, until the 1950s, at
both the federal and provincial governmental levels, viewed land and
other resources as unlimited. The late but inevitable realization that
this was not the case led the Canadian federal government to initiate
a national inventory of land and other natural resources. The purpose
of what was christened as the "Canadian Geographical Information
System" in 1963-64 was to analyze data collected by the Canada
Land Inventory (CLI) to produce statistics that could be used to
develop land management plans for effective resource utilization in
large areas of rural
 Canada.[95]
 The CLI produced seven maps classifying
 land.[96]
 Constructing the CGIS meant developing techniques for the rapid
handling and analysis of these maps and the data on which they were
based. Today's commercial packages GIS parasitize key conceptual and
technical innovations of the CGIS. At the technical level, when the
CGIS project was initiated, there was no prior experience on how to
structure geographical data internally (within the computer); there
were no techniques for the overlay of maps or for calculating area. An
experimental scanner to scan map data had yet to be built.




Among conceptual innovations, the most important was the distinction
between: (a) the data used to draw the polygons forming the boundary
of a place (locational information); and (b) the set of features it
has, that is, its attributes. Polygons need not have the same size or
geometry. When ecological populations and communities are modeled in a
GIS framework, explicit asymmetric irregular spatial information can
be incorporated without unrealistic simplifying assumptions such as
that of representing the spatial structure as a square or some other
regular geometric grid. The exploitation of this possibility takes
spatially explicit ecological modeling beyond its traditional confines
in which the only spatial structures that could be considered are
those with regular geometries. Though GIS-based ecological modeling is
still in its infancy (and an early example will be discussed in the
next paragraph), it is clear that these techniques will allow the
construction of spatially-explicit ecological models at a level of
detail that was impossible before. Moreover IBMs can now be
constructed with such detailed spatial representation. The confluence
of IBMs and GIS is arguably the most fecund area of ecological
modeling today.




Equally as important as the distinction between polygon and attribute
was the decision to "vectorize" the scanned
images. Scanned images gave "raster" data, that is, data
in the form of regular grid points which either do or do not possess a
specific property, for instance, the presence of a given vegetation
type. Vectorization is the replacement of these point-based structures
by lines that are naturally interpreted (such as boundaries of habitat
types). What is critical is that these lines can then be joined to
form polygons. Raster data can be obtained from a variety of sources
including maps and photographs; in the present context what is
critical is that raster data can be obtained by remote sensing through
satellite imagery from which the distribution of many vegetation and
soil types can be inferred. As early as 1989 Running and several
collaborators estimated the annual evapotranspiration and net
photosynthesis for a 28   55 sq km region of Montana using a GIS
software
 package.[97]
 The study region was divided into 1.1   1.1 sq km cells defined
by satellite sensor pixel size. The GIS package was used to integrate
topographic, soil, vegetation and climatic data from a variety of
sources including the results of remote sensing. Ecological
assumptions entered into the models that were then used to predict
evapotranspiration and net photosynthesis. The results obtained were
in fairly good agreement with field data.




Within ecology the use of GIS-based models is the analog of visual
modeling in other
 sciences.[98]
 It is no longer controversial that visual representation, at least as
a heuristic, offers resources for scientific innovation not offered by
purely linear representations (such as linguistic or mathematical
representations). GIS-based models constitute two-dimensional visual
representations of ecological systems. It is likely that these
representations incorporate spatial insights that will result in new
and fecund directions for ecological modeling to take.




Nevertheless, what is somewhat philosophically troubling about the use
of GIS in ecology is the conceptualization and representation of
geographical information as: (a) a linked set of places, linked in the
sense that the places must maintain fully precise adjacency relations;
but (b) an unlinked set of attributes (for instance, the presence or
absence of species or other biological features). There is something
disarmingly natural about this: it certainly seems to capture the
geographical rootedness in place that lies at the basis of planning
for biodiversity conservation. But this choice of representation has
its costs: the mode of representation which is at the core of GIS
makes it "natural" to represent systems in such a way that
certain types of relationships tend to get lost, or at least relegated
to the background, while others receive emphasis. Consider the
following example. Carnivores cannot be present at any place unless
prey species also exist. This trivial and obvious ecological fact
cannot be explicitly represented using the standard resources of any
GIS package (that is, it cannot be represented without writing special
programs). Attributes are represented without relations between
them. This encourages, though does not require, analyses that do not
use relations between attributes. (Obviously, one can start with a
GIS-based representation and add other relations as part of the
superstructure of the model.) Philosophers of science have long known
that modes of representation influence the introduction and
development of conceptual systems based on them. GIS may have such an
influence through representational choices that guides ecology down a
path where relations between attributes receive less emphasis than
they would in traditional ecological models.


## 6. Concluding Remarks




The diversity of the conceptual issues discussed in this entry
preclude any compelling general conclusions to be drawn at this stage
of the development of the philosophy of ecology. Even the defense of
reductionism in ecology using IBMs can with so many attendant
qualifiers that it cannot be regarded as an unequivocal endorsement.
That GIS models seem to introduce a new type of visual representation
in the sciences is another conclusion that requires similar
qualification and further scrutiny before it is accepted. It should
also be clear from the preceding discussion that ecology *may*
provide a fertile ground for the exploration of concepts of complexity
and systematicity that have recently begun to engage philosophers and
scientists
 alike.[99]




However, the inability to draw general philosophical conclusions about
ecology is at least as much due to the relative lack of philosophical
scrutiny of ecology as it is due to the nature of the subject. Even
within the philosophy of biology, ecology has received little
attention compared to other sub-disciplines of biology, especially
evolution, and, lately,
 development.[100]
 Ecology deserves better. As this entry has emphasized, there are many
foundational and conceptual issues within ecology that can be
clarified and better framed through carefully philosophical
analysis. The diversity-stability problem is exemplary in this
respect. The relevance of ecology to human well-being and
biodiversity should also be obvious-- see
 conservation biology
 and
 environmental ethics.
 But, even leaving aside these narrower considerations, ecology
provides ample opportunity to illuminate general questions about many
of the traditional themes of the philosophy of science: complexity,
contingency, holism, lawlikeness, reductionism, representation,
etc. This entry is an invitation to philosophers to take an active
interest in ecology.