Download Mechanistic Approaches to Community Ecology

AMER. ZOOL., 26:81-106 (1986)
Mechanistic Approaches to Community Ecology: A New Reductionism?'
THOMAS W. SCHOENER
Department of Zoology, University of California,
Davis, California 95616
SYNOPSIS. Mechanistic approaches to community ecology are those which employ individual-ecological concepts—those of behavioral ecology, physiological ecology, and ecomorphology—as theoretical bases for understanding community patterns. Such approaches,
which began explicitly about a decade ago, are just now coming into prominence. They
stand in contrast to more traditional approaches, such as MacArthur and Levins (1967),
which interpret community ecology almost strictly in terms of "megaparameters."
Mechanistic approaches can be divided into those which use population dynamics as a
major component of the theory and those which do not; examples of the two are about
equally common. The first approach sacrifices a highly detailed representation of individual-ecological processes; the second sacrifices an explicit representation of the abundance and persistence of populations.
Three subdisciplines of ecology—individual, population and community ecology—form
a "perfect" hierarchy in Beckner's (1974) sense. Two other subdisciplines—ecosystem
ecology and evolutionary ecology—lie somewhat laterally to this hierarchy. The modelling
of community phenomena using sets of population-dynamical equations is argued as an
attempt at explanation via the reduction of community to population ecology. Much of
the debate involving Florida State ecologists is over whether or not such a relationship is
additive (or conjunctive), a very strong form of reduction. I argue that reduction of
community to individual ecology is plausible via a reduction of population ecology to
individual ecology. Approaches that derive the population-dynamical equations used in
population and community ecology from individual-ecological considerations, and which
provide a decomposition of megaparameters into behavioral and physiological parameters,
are cited as illustrating how the reduction might be done. I argue that "sufficient parameters" generally will not enhance theoretical understanding in community ecology.
A major advantage of the mechanistic approach is that variation in population and
community patterns can be understood as variation in individual-ecological conditions. In
addition to enriching the theory, this allows the best functional form to be chosen for
modeling higher-level phenomena, where "best" is defined as biologically most appropriate rather than mathematically most convenient. Disadvantages of the mechanistic
approach are that it may portend an overly complex, massive and special theory, and that
it naturally tends to avoid many-species phenomena such as indirect effects. The paper
ends with a scenario for a mechanistic-ecological Utopia.
INTRODUCTION
1
ecology, and ecomorphology—as the basis
constructing a theoretical framework
Wlth w h l c h to
interpret the phenomena of
community ecology. The approach contrasts Wlth t h e
"descriptive" one, in which
community phenomena are represented by
models with no lower-level derivations, but
which have one or both of
descriptive
prowess and mathematical convenience. Of
course, any given work may contain both
mechanistic and descriptive elements,
Hence m
principle there exists a continuum
of mixtures, although the distribution
of actual
studies may be bimodal. Somewhat informally, I will use "mechanistic"
to
to refer
refer to
to studies
studies primarily
primarily employing
employing the
the
h, T-.
h U dJ •
.„ hi
• •
•
for
Community ecology is chronically among
the most tumultuous and at the same time
alluring of ecology's subdisciplines. Presently there is as much controversy over
method as over fact (see symposia volumes
by Strong et al, 1984; Price et al, 1984).
During the past decade or so, a rather distinct methodology in community ecology,
called the mechanistic approach, has been
slowly coming into prominence. This
approach can be most simply denned as the
use of individual-ecological concepts—
those of behavioral ecology, physiological
,
From the Symposium on Mechanistic Approaches to
theStudyofNaJalCommunitiespresemedJLAnn^l
Meeting of the American Society of Zoologists, 2730 December 1983, at Philadelphia, Pennsylvania.
mechanistic approach. T h e designation
mechanistic has been used in disciplines
Other than ecology, inevitably with differ81
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THOMAS W. SCHOENER
ent meanings. For example, within behavior, it sometimes implies casting an explanation in terms of physiological or
structural constraints rather than according to evolutionarily selected goals. Our
usage of the term seems to have arisen
gradually during the past decade or so, with
independent roots in a number of the
papers described in the examples section
below. The movement appears to be one
symptom of the increasing "particularization" of community ecology, in which biological differences (among others) between
various systems are emphasized rather than
ignored (Colwell, 1984; Schoener, 1985a).
Disappointingly but not surprisingly,
when the idea of this symposium was first
exposed to the scientific public, there was
substantial opposition. Two criticisms stood
out. First, the mechanistic approach was
deemed indistinguishable from the way
ecology was normally performed; it had
nothing new to offer—everyone did it as
it was. Second, the mechanistic approach
was pejoratively labeled "reductionist";
reductionist approaches have never worked
in science, and they will not in community
ecology either.
No matter how cathartic, it is seldom
wise to devote most of an article to rebutting one's grant reviewers. However, these
two criticisms provide such excellent
departure points for discussing the whole
subject of mechanistic approaches that I
have yielded to the temptation, as follows.
The first part of this article describes in
some detail a well-known approach to community ecology, that dealing with limiting
similarity, which in original form is shown
very close to the nonmechanistic end of the
spectrum. This approach is contrasted in
its procedures with the principal kinds of
mechanistic approaches, using examples
that include other papers in this symposium. Next, the question of reductionism
is addressed. To do this, it is necessary to
outline what philosophers of science mean
by reductionism. To apply their conditions
to ecology, it is useful first to decide upon
what the various levels of ecology are, inasmuch as this is possible. The questions can
then be asked, is the mechanistic approach
reductionist, and to the extent that it is, is
this a drawback or an advantage, or does
it not matter? In performing this evaluation, I will be interested in what scientists
and philosophers have said about reductionism in other areas of science, and
whether characteristics of reductionism in
general are usefully applied to issues concerning community ecology. Much discussion has already ensued over the question
of whether biology can be reduced to physics and chemistry (e.g., papers in Ayala and
Dobzhansky, 1974). More recently, a fair
amount of discussion of the possible reduction of Mendelian and population genetics
to molecular genetics has taken place
(papers in Sober, 1984). However I think
mine is the first paper considering in detail
the possible reduction of community ecology, so that I am scouting new ground and
will doubtless draw some fire.
EARLY APPROACHES TO LIMITING
SIMILARITY: A NONMECHANISTIC
METHODOLOGY
As the mechanistic approach has recently
begun to infiltrate limiting-similarity concepts, we can obtain a purer example of
nonmechanistic methodology by concentrating on relatively early papers. To this
end, I will deal mainly with the 1967 paper
by MacArthur and Levins (hereafter
referred to as ML).
The basic objective of ML is to specify,
given a two-species community, whether a
third species can invade; that is, whether
its population will increase when initially
rare to some stable equilibrium. The resident species are envisioned as being
arranged along a single resource dimension with respect to the distribution (called
the utilization) that describes how a species
population uses resources. An example of
a resource dimension is prey size, and the
utilization can be thought of as a frequency
histogram of population use for the various
prey-size categories. The specific question
of ML is whether or not the invading species
can be sandwiched between the two residents. No changes of any kind in the utilizations are allowed; utilizations are taken
as given.
ML proposes that to answer this question it is sufficient to know a ratio of carrying capacities (specifically, that of the
invader to that of either resident, the two
COMMUNITY ECOLOGY
latter being assumed identical) and a quantity labeled d/w. This quantity is the ratio
of the distance between utilization peaks
(assumed constant) to the niche width (the
standard deviation of the utilization, again
assumed identical for all species). Carrying
capacities (K's) would be hard to measure
in nature without an experiment, but in
principle they can be measured. Where they
are not measured, or for simplicity, they
can be assumed equal for all species, in
which case their ratio equals one. Utilizations are typically computed (as in Levins
[1968]; also Mac Arthur and Levins [1967])
by observing a species population and noting the frequency of the population's
aggregate use of resource items in the various resource categories. For example, if
the resource dimension were prey size, one
would observe (say by gut analysis) what
food items a representative set of individuals from the population have eaten, then
count the number of such items in each
food-size category (say 1.1-2 mm, 2.1-3
mm, etc.) and divide by the total number
of items to obtain the frequencies (the p's).
In short, ML offers a very simple protocol
to determine quantitatively the possibility
of community invasion.
To understand how this protocol can be
so simple, we need to sketch its derivation.
Assume that species are competing according to the Lotka-Volterra competition
equations. Assume that the parameters a
in these equations can be calculated as
2
(1)
where pjh is the frequency of the utilization
of Species i in Resource Category h. Then
the Lotka-Volterra equations are used to
calculate those combinations of a's and K's
just allowing coexistence (the limiting similarity). With the use of eq. 1, those combinations can be related to the p's, which
compose the utilizations. Finally, assuming
that the utilizations are normally distributed, the p's in turn can be related to d/w,
the ultimate unit in which limiting similarity is expressed.
Note that this derivation, while using
population-dynamical equations, does not
83
require that we draw on the data or theory
of behavioral or physiological ecology. It
is possible to justify eq. 1 in behavioral
terms, but in fact the simplest such justification involves many more parameters
than the p's (Schoener, 1974a); the p's
occur as a kind of scaffolding around which
these other parameters are placed (see
below). Eq. 1 then is justifiable only as a
limiting case, i.e., when all additional
parameters are identical and cancel out.
Moreover, it is possible to interpret the
Lotka-Volterra equations in behavioral or
physiological terms, but only at equilibrium for resource competition (see below),
and in any event this was not done either
in the equation's original derivation or in
ML.
Persons not familiar with community
ecology may at this juncture have the
impression that the ML protocol has been
demonstrated to work enough of the time
so that it can be called an "experimental
law" sensu Nagel (1961; see below), or at
least some assumptions used in its derivation can be called experimental laws. In
fact, as is so often the case in community
ecology, these are more nearly "hopeful
laws" rather than laws; they are proposals
about nature that remain, for the most part,
to be supported with evidence. As we shall
illustrate in part, they have sometimes not
in fact been supported for particular systems—it is a matter of some debate whether
they can even be called "statistical laws."
Nonetheless, they do constitute theoretical
concepts, and their lack of strong support
does not prevent us from examining them
from a philosophical standpoint.
Five problems with the assumptions used
in the derivation of the ML protocol may
help explain its tenuous position with
respect to nature (see also Abrams, 1983).
1. The assumption that utilizations are
normal, when relaxed, can change radically the d/w just allowing coexistence
(Roughgarden, 1974).
2. The assumption that all utilizations
are identical, when relaxed, allows variation from the original predictions
(McMurtrie, 1976).
3. The assumption that a can be calculated using eq. 1, when relaxed, can give
quite different values of limiting d/w; there
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THOMAS W. SCHOENER
is a systematic tendency for sensible variation in eq. 1 to give smaller d/w's (Abrams,
1975).
4. During the process of competition,
individuals may modify their per-unitresource consumption rates, so that utilizations will not be constant.
5. The Lotka-Volterra equations may
not actually describe the population
dynamics of competition, in which case
there may be no "a" as such, and either a
linear approximation only valid near equilibrium must be used or a whole new
approach invented (see below and Abrams,
1980a).
Although numerous other complications exist (see Abrams, 1983; Schoener,
19856, for reviews), I have listed the above
five, in order of increasing potential for
undermining the ML protocol, to set the
stage for a demonstration of how behavioral and physiological information can be
used to determine their seriousness. To
evaluate Points (1) and (2), it would be
essential to know something about the consumption of resources at the individual
level—for example, when are utilizations
leptokurtic (peaked, with thin tails) in terms
of the feeding behavior of individuals? To
evaluate Point (3), we would like to know
what set of assumptions concerning how
individuals use resources and how energy
gained from that use is converted into offspring will give eq. 1 literally, and what will
not. We need to know the same kind of
information to evaluate Point (5). To evaluate Point (4), we need to know (say from
foraging theory) whether the probability
of consuming a unit of a particular resource
kind changes as the number of such
resource units is affected by the action of
competing individuals.
We shall now see to what extent mechanistic approaches to community ecology
help avert these and similar problems characteristic of nonmechanistic approaches.
MECHANISTIC APPROACHES: SOME
EXAMPLES
I would like to divide mechanistic
approaches to community ecology into two
groups, those which explicitly include population dynamics in their formulation and
those which do not. As the latter are somewhat more straightforward than the former, I begin with them.
Approaches that exclude population
dynamics
1. Werner and Hall (1976, 1977, 1979;
Werner, 1984). Ecologists have long
observed that given species have different
habitat distributions in the presence than
in the absence of other species; in particular, habitats of sympatric forms are often
less similar than those of allopatric forms
(review in Schoener, 1985). This phenomenon has been investigated in two ways.
First, habitat distributions from places
where a given species does or does not
coexist with one or more other species are
compared (e.g., Crowell, 1962; Keast, 1970;
Schoener, 1975; Lister, 1976). Second,
experimental addition or removal of species
is performed and subsequent habitat shifts
noted (review in Schoener, 1983). The
commonest explanation for habitat shift is
that it results from some kind of competition, either consumptive (via resource
depletion), pre-emptive (via passive competition for space), or some form of interference (e.g., territorial, encounter; all
terms sensu Schoener, 1983). Such competition could act at the behavioral level
or at some higher level. Patterns discovered by the comparative approach are typically the end result of ecological or even
evolutionary processes. Most experimentally produced habitat shifts, in contrast,
must necessarily be behavioral responses,
given their short-term nature. Even for
those, however, the exact mechanism of
the competition, and a clear idea of how
to predict the occurrence and degree of
habitat shift, remained largely unknown
until the work of Werner and Hall with
sunfish.
Werner and Hall related habitat shift to
optimization of feeding intake by individual fish, as follows. First, they showed that
individuals of each of three species when
by themselves preferred the vegetated
habitat. Then they placed individuals of
the species together and found that those
of one species shifted into open-water habitat, those of a second shifted into bottom
COMMUNITY ECOLOGY
habitat, and those of the third stayed in the
vegetated habitat. They calculated the
energetic gain per unit time of staying in
the vegetated habitat with a competitor vs.
moving to a new habitat. By assuming that
individuals maximize their rate of net food
energy intake per unit feeding time, they
correctly predicted which species would
shift out of the vegetation and the point
(in terms of food density) at which each did
so. These experimental results paralleled
the habitat distributions of the species in
unmanipulated field situations. Small discrepancies in the predictions were attributed to aggressive behavior between individuals.
2. Pulliam (1986). Pulliam is concerned
with seasonal shifts in niche overlap among
coexisting sparrow species. Such shifts have
been explained from two intersecting theoretical perspectives. One perspective is
behavioral: the compression hypothesis
(MacArthur and Wilson, 1967) and related
ideas of foraging theory predict, again on
the basis of maximizing energy per unit
time, the nature of diet and habitat shifts
and expansions. The other perspective is
evolutionary; the morphological and
behavioral adaptations of individuals are
constraints on their feeding abilities, in the
sense that different phenotypes have different feeding costs over an array of food
and habitat types. Given that such adaptations have evolved in response to conditions during one season rather than
another, seasonal variation in ecological
overlap can be given an evolutionary explanation (Schoener, 1982). By measuring
certain behavioral quantities necessary to
evaluate feeding profit, e.g., husking time,
Pulliam was able to show that both theoretical perspectives contribute toward
explaining niche overlap in sparrows.
3. Price (1986; Price and Waser, 1984,
1985). Price's work is similar to Werner
and Hall's but is framed somewhat more
generally. Various heteromyid rodent
species show different habitat distributions
in the North American Sonoran desert.
These distributions can be hypothesized as
resulting from 1) differential abilities to
harvest resources in different habitats (as
in Werner and Hall, 1979) or 2) differen-
85
tial abilities to avoid predators in different
habitats. By a study of individual feeding
abilities on types of seeds found in the various habitats, by creating artificially
"seeded" habitats in nature and examining
subsequent heteromyid preferences, and
by detailed field observation of individual
escape abilities in the various habitats, Price
hopes to demonstrate which, if any, of the
two hypotheses explains the heteromyid
habitat distributions in nature. Results to
date indicate that different species selected
patches with different soil textures, in turn
interpretable on the basis of rodent size
and locomotory attributes. Predictable
habitat shift by the rodents was induced by
artificially altering soil-patch types (Price
and Waser, 1985).
4. Moermond (1979, 1986). The ML
approach as outlined above is not concerned with the question of why utilizations are placed where they are on the
resource axis, or why they have the shape
they do. To explore such questions, one
can take a variety of tacks, some of which
are nonmechanistic. For example, by using
certain very general assumptions about how
evolution works (e.g., it maximizes population density, Roughgarden, 1972), shifts
and other changes in utilizations that are
responses to competition can be modeled.
Initial attempts in this direction (e.g.,
Roughgarden, 1976; Slatkin, 1980) did not
use individual-ecological concepts to justify their assumptions; the latter were
sometimes induced from observations, and
in that sense were "experimental laws,"
albeit highly statistical ones. Other
assumptions (e.g., like eq. 1) were simply
assumptions, not well justified empirically
or conceptually. More recent theoretical
approaches to evolutionary changes in utilizations do use individual-ecological justification for certain assumptions (e.g., Case,
1982).
None of the approaches just discussed,
whether entirely or partly nonmechanistic,
is concerned with the degree to which,
independently of resource abundance, certain resource categories are more effectively used by consumers than others.
Moermond's approach is to analyze habitat
dimensions from a functional-morpholog-
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THOMAS W. SCHOENER
ical standpoint; he asks what sorts of habitat distributions for individuals are and
are not likely, given functional-morphological constraints. For example, no matter
what their special adaptations, arboreal lizards are able to jump so far and no farther;
this limits how they get around in the vegetation, in turn constraining the degree of
generalization possible. Moermond's
approach identifies a number of locations
in habitat-niche space that are relatively
"easy" to occupy purely from the standpoint of locomotory abilities. These locations give an expectation of where in a noncompetitive niche space species utilizations
are most likely to be found. This initial
picture forms a mechanistically based null
expectation to which competitive effects
can then be added where necessary to produce the final community configuration.
5. Schluter and Grant (1984). Schluter
and Grant's approach is very similar to
Moermond's in its general objectives.
Rather than lizards, however, Schluter and
Grant studied finches, and rather than
being concerned with habitat dimensions,
they were concerned with the food-size
dimension. They were able to show that
variation in beak depth within and between
species from one Galapagos island to
another is predictable (with a few exceptions) from variation in the availability of
differently sized seeds. The necessary calculations for such predictions involve
behavioral data in a fundamental way: seedharvesting abilities of individuals having
different beak sizes are measured (Schluter, 1982), and a particular sized individual
is expected on a given island which can
attain the highest population density (vide
Roughgarden, 1972) for its particular
spectrum of seed sizes. Schluter et al. (in
preparation) were able to use these results
as a basis with which to examine evolutionary effects of interspecific competition.
Approaches that include population
dynamics
The preceding five approaches by and
large do not include population concepts
as major components in their theoretical
accounting for community phenomena, a
partial exception being the assumption
Schluter and Grant make about density. In
particular, no study explicitly employs population dynamics—the dynamics of birth
and death, and of immigration and emigration—as part of a theoretical explanation.
The major problem in not so doing is as
follows. Individuals may optimize some
aspect of their performance, e.g., maximize
net food-energy intake per unit feeding
time, yet not be able to maintain a population in the habitat of interest. Ultimately,
what determines the abundance of a species
in some place is how net food energy is
converted into offspring, and how that
energy can be used to promote survival in
comparison to how survival is reduced by
the increased exposure to predation or
other risks that foraging entails. Because
there are often very strong relationships
between optimizing a behaviorally defined
currency and maximizing individual fitness
(Schoener, 1971), which itself can be
related to population characteristics, this
problem, while serious, is far from a fatal
flaw.
A second problem is that concentration
on individual behavior and physiology
emphasizes what has been called (Van
Valen, 1965; Roughgarden, 1972) the
within-phenotype component of population variability. Phenotypes are treated
seriatim: they are compared with respect
to their fitnesses but are essentially considered as alternatives rather than as possibly
coexisting in a population. However, to
understand completely why a population's
utilization has a particular set of properties, one needs also to know the betweenphenotype variation, and the latter is very
much of a population-ecological trait,
determined by the scheduling of births and
deaths as it affects age structure, by how
constraints such as sexual reproduction
affect the inheritance of phenotypic variability, and so on.
A second class of approaches, the one
we are about to discuss, explicitly includes
population phenomena, particularly population dynamics, as a major theoretical
tool. Approaches of this class, however, are
not necessarily superior to those discussed
in the last section; they too entail certain
COMMUNITY ECOLOGY
disadvantages, especially for higher organisms. Evolutionary aspects are often not
considered of primary interest; rather,
traits are plugged into ecological models
with little interest in their evolutionary
underpinnings. Moreover, such individualecological complexity typically must be subsumed into a limited number of population
parameters—while the models used in
population-dynamical approaches are based
on mechanistic considerations, they still
often simplify greatly. For example, the
shifts in resource type (e.g., seeds, habitats),
which are the fundamental phenomena
certain of the above studies are trying to
understand, are assumed not to occur in
many of the population-dynamics approaches. Because extant differences
between these two approaches are more a
matter of emphasis than anything else, the
two sorts of approaches might ultimately
be combined, a union that has been mostly
avoided up to now probably because of the
increased complexity that would be
entailed. As we shall see below, certain
studies provide partial exceptions to this
generalization.
6. Tilman (Titman, 1976;Tilman, 1977,
1982, 19866). Tilman's (Titman, 1976;
Tilman, 1977) research with diatoms concerned the question of the coexistence of
competitors, the same question attacked by
MacArthur and Levins. Tilman's approach
was quite different, however. He took
advantage of the fact that growth of many
microorganisms on specific nutrients can
be represented by the Michaelis-Menten
equation, a relatively simple model having
only two parameters. By determining
Michaelis-Menten parameters for each of
two species of diatoms on each of two
nutrients, he could predict under what
combined nutrient regimes one or the other
species would win in competition, and
under what regimes the species would
coexist. Coexistence occurs under combinations in which each diatom species has
its growth limited by a different nutrient.
In addition to verifying his predictions in
laboratory experiments, Tilman could
show that certain phenomena in the field
are fairly consistent with them (but see Sell
et al, 1984; Tilman et al., 1984).
87
Tilman (1982, 1986a, b) later extended
his ideas to terrestrial herbaceous plants.
He is engaged in testing the general proposition that spatial variation in the identity
of the dominant species can be explained
with mixed-nutrient models similar to those
used for diatoms. So far in such models,
only the equilibrium situation is considered: the dynamics leading to equilibrium
are not made explicit. Nonetheless, the
models approach community questions via
population models in turn based on physiological considerations.
7. Schoener(1973, 19746, 1975, 1976,
1978). Over the past decade, I have developed a set of competition models that are
alternatives to Lotka-Volterra models.
Unlike the latter, the alternative models
typically generate non-linear zero-isoclines. Using relatively simple terms, they
attempt to represent the various components of competition more isomorphically
than does the Lotka-Volterra model, used
primarily because of simplicity and supposed descriptive prowess. For example,
the simplest term for resource division is
I/(N, + /3N2), where I is the amount of
resource to be divided, N, and N2 are the
number of individuals of Species 1 and 2,
respectively, and /3 is the ratio of the
resource consumption rates of an individual of each of the two species (Schoener,
19746). By contrast, interference is assumed
proportional to terms in Nj, as would be
the case for "encounter" competition, in
which deleterious interactions between
individuals occurred randomly. More complicated terms for resource division can be
derived from submodels of how competitors consume resources. Other terms concerning how energy is converted into offspring also occur in the models, although
these are very primitive. The models were
initially tested in a study of habitat shift in
Anolis lizards; more recently, they have
been tested by Belovsky (1984a, b, 1986)
and Rasmussen (1983), among others.
Their common characteristic, concave
zero-isoclines, is in qualitative agreement
with most competition data from the literature (e.g., Ayala et al., 1973; Quinn, in
preparation).
A related set of models, in which inter-
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THOMAS W. SCHOENER
ference is the only form of competition,
leads exactly to the Lotka-Volterra equations (Schoener, 1973, 19746). In the sense
to be explained below, this constitutes a
derivation of the Lotka-Volterra model and
has as a by-product the decomposition of
its parameters, r, K and a, in terms of
microparameters describing feeding abilities and time budgets, themselves parameters of behavioral ecology.
8. MacArthur(1968), Schoener (1974a,
b). In contrast to the models discussed in
the last section, which all involve a single
trophic level, MacArthur represented the
dynamics of each consumer and resource
by a separate equation. The behavior of
such an equation system away from equilibrium can be very complex (Case and Casten, 1979), but as MacArthur showed, the
equations can be collapsed at equilibrium
into Lotka-Volterra form. In the same sense
as just discussed, this is also a derivation of
the Lotka-Volterra model, but at equilibrium only (see Schaffer, 1981, however, for
conditions on its approximate validity elsewhere). Again, one may obtain expression
for r, K and a in terms of microparameters,
but these are valid only at equilibrium. The
expression for a is of special interest.
Schoener (1974a) later showed that it contained the utilizations (the p's) in exactly
the same places as they occur in eq. 1, but
in the company of various other parameters having to do with resource availabilities, overall rates of consumption by individuals, and conversion rates of food into
energy.
A similar collapse of another set of consumer-resource equations was performed
by Schoener (19746); this yielded a derivation at equilibrium of some of the nonlinear-isocline equations discussed in the
previous section.
9. Holt (1983). In an entirely theoretical treatment, Holt investigated the constraints that optimal foraging of individuals places on population models of
predator-prey interactions. Holt assumed
two prey and one predator and analyzed
the shape of the predator isocline. This
isocline is a curve connecting all values of
the number of Prey Species 1 and the number of Prey Species 2 that give a fixed yield
to the predator. Ecologists have generally
assumed that this isocline has negative
slope; when it has positive slope, curious
effects can occur, such as one predator
increasing its population size when another
predator eats more of one of its prey. Holt
concluded that while many assumptions
about foraging behavior do lead to negatively sloped predator isoclines, predators
that must spend much time discriminating
between prey of unequal value or that show
switching behavior (Murdoch, 1969) may
give positive slopes. In short, Holt explicitly related feeding-behavior considerations to population-community models.
10. Abrams (1982); Sih (1984). The
"functional response" describes the relationship between the number of prey consumed per predator and prey density. The
form of the functional response has profound consequences for the stability of
predator-prey interactions, and ultimately, for community composition (Murdoch and Oaten, 1975). Theoretical papers
by Abrams (1982) and Sih (1984) explore
how various feeding strategies affect the
functional response curve. Abrams showed
that the proportion of time spent feeding
makes a great deal of difference to the shape
of the functional response. Sih examined
how variation in foraging area, foraging
speed, overall foraging time, and time spent
foraging in risky but energetically profitable places affects the functional response.
In general, if these behavioral parameters
increase with prey density, a functional
response that stabilizes the predator-prey
interaction is favored. Time minimizers
(sensu Schoener, 1969) and certain classes
of energy maximizers (e.g., those not near
satiation) are argued likely to show this
increasing relation and hence to contribute
toward community stability. As in Holt's
(1983) paper, explicit connections are made
between behavioral parameters and those
of population dynamics; the former are
used to characterize variation in the latter.
11. Belovksy (1984a, 1986). Like Tilman, Belovsky was able to show that the
outcome of competition could be quantitatively predicted using mechanistic equations in which the parameters were those
expected from individual-ecological con-
COMMUNITY ECOLOGY
siderations. Unlike Tilman's diatoms, however, Belovsky dealt with organisms at the
peak of the phylogenetic scale, i.e., mammals. Belovsky first fitted several of Schoener's (19746) resource-competition models,
and the Lotka-Volterra model, to experimentally generated field data on competition between moose and hare. The free
parameters were simultaneously adjusted
by nonlinear regression to produce the best
fit to the data, which comprised the population sizes of each species. He determined that the model having overlapping
and exclusive resource terms was best.
Using the empirically determined parameters of that model, he was able to obtain
estimates of the amounts of overlapping
and exclusive resource. Belovsky next calculated the same quantities from his model
of food selection, a model which he had
previously shown (Belovsky, 1978, 19846)
to fit dietary data from individual moose
and hare. The quantities calculated by this
second, entirely independent, method were
very similar to those obtained from fitting
the population data, thereby providing a
striking consistency.
In a separate analysis, Belovksy (1986,
in preparation) used similar models to predict the number and sizes of generalized
herbivore species that can coexist in a community. Here, food quality and food size
were the axes of resource separation, and
limitations of an individual's physiology and
morphology determined how much of the
resource could be utilized by a given sized
herbivore. In particular, large herbivores
require large food items (large bulk per
bite) but can get away with eating food of
low quality (low digestibility); small herbivores are the opposite. The handling time
of individuals, their metabolic requirements and their digestive rates are all relevant to determining each sized herbivore's position on the two resource axes.
Here, behavioral and physiological quantities are related to community ecology in
an entirely explicit way.
12. Abrams (19806, 1981a, 6). Another
very detailed application of a mechanistic
competition model was performed by
Abrams on hermit crabs. These organisms
use empty molluscan shells for protection,
89
and as such shells are generally scarce, they
are often proposed as a limiting resource.
To investigate this proposal precisely,
Abrams (19806) first constructed a differential equation describing the dynamics of
the shell resource. This expression at equilibrium was used to derive a "competition
ratio," similar to the competition coefficient discussed above, which like the latter
expresses the relative intensity of inter- to
intraspecific competition, all taken percapita. The competition ratio was checked
by marking shells and following their fate
in the field (Abrams, 198 la); the two methods gave very similar results. Competition
ratios were then calculated for species in
various communities and found to be lower
than those anticipated from previous studies on other organisms (Abrams, 1981a, 6).
In short, this study used a mechanistic
model, tailor-made for the hermit-crab system, in place of the ML protocol to determine relative competition intensity and
thereby help explain the identities of coexisting species.
13. Spiller (1986). Very recently, Spiller
was able to evaluate experimentally the
simple ML expression (eq. 1) for the competition coefficient and the more complicated expression that Schoener (1974a)
derived from the Mac Arthur (1968) consumer-resource equations (#8 above).
Using field observations, Spiller separately
evaluated each term in the two expressions
for two syntopic spider species in a salt
marsh. He then did a series of controlled
manipulations in which he was able to measure directly the magnitude of the competition coefficient. The more complicated
Schoener (1974a) expression matched the
experimentally determined a's rather well,
but the ML expression (eq. 1) failed even
to match the relative magnitudes of the a's.
Thus the more mechanistic expression was
shown far superior in predicting the effects
of competition.
In conclusion, the above review (which
is incomplete) shows that mechanistic
approaches are used in a wide variety of
systems, are mostly very recent, and can
take on various specific methodologies. The
discussion also makes clear that our meaning of the term "mechanistic" is in some
90
THOMAS W. SCHOENER
ity, in which terms not in the to-be-reduced
science must be related to terms in the
reducing science. These relationships can
take three forms: 1) logical connections
between established meanings, 2) conventions, or definitions; and 3) facts, i.e., relations established empirically. The second
condition is derivability, in which all laws of
the to-be-reduced science, including those
with terms not in the reducing science, must
be derivable from the theoretical princiREDUCTIBILITY OF COMMUNITY ECOLOGY
ples of the reducing science, using where
Formal conditions for the reduction of
necessary the connectability relationships.
one area of science to another
Various difficulties with Nagel's criteria
While most philosophers (review in have been summarized by Wimsatt (1980a;
Wimsatt, 1980a) now advocate a broader see also Hull, 1976). On the one hand, the
concept of reductionism than did Nagel use of approximations makes a literal exe(1961) in his classic work, Nagel's formal- cution of Nagel's conditions problematical
ism, because of its precision and primacy, (in what sense are they logical deductions?)
is an attractive place for our discussion to and jeopardizes most conceivable realizabegin. In general, "a reduction is effected tions in actual science. On the other hand,
when the experimental laws of the second- if such difficulties can be overcome, the inary science (and if it has an adequate the- principle satisfying of Nagel's conditions
ory, its theory as well) are shown to be the may generally be possible, even though the
logical consequences of the theoretical in-practice realization may be too complex
assumptions . . . of the primary science" to be useful to science and may merely sat(Nagel, 1961, p. 352). By experimental law, isfy a philosophical desire for ontological
Nagel means generalizations about the simplification. Because scientists seem often
phenomena of the science in question, to show reductionist behavior, broadly
whether they be absolute or statistical. An construed, Wimsatt (19766, 1980a) argues
example of an absolute experimental law that rather than emphasizing Nagel's idealis "lead melts at 327°C." Nagel distin- ization, we should adopt a broader charguishes "theoretical laws" or "theories" acterization which corresponds more
from experimental laws (I.e., p. 32) as state- closely to the actual practice of science.
ments "whose basic terms are not associ- Toward this end, Wimsatt (1976a, b) proated with definite experimental proce- poses "explanatory" reduction, in which
dures for applying them . . . so that a theory one has "an explanatory relation between
cannot be put to direct experimental test" a lower-level theory or domain of phenom(I.e., p. 85). An example of a theoretical ena and a domain . . . of upper-level phelaw would be a statement dealing with the nomena" (Wimsatt, 19766, p. 220). Furatomic theory of matter. Nagel admits the ther, one "attempts to identify or explain
distinction is in some ways not hard and the upper-level whole and its properties
fast, and given the statistical and often with or in terms of a configuration of lowerweakly verified nature of "laws" in ecol- level parts and their known monadic or
ogy, it is even looser here than it is in phys- relational properties" (Wimsatt, 19766, p.
208). A consequence of Wimsatt's modiics and chemistry.
fication is that practical aspects of reducA problem with reduction can be that tionism, e.g., "research strategies," are
the laws of the science to be reduced may emphasized over formal aspects; if the
contain terms not found in the reducing lower-level characterization becomes too
science. Under such circumstances, reduc- intricate or cumbersome, it is no longer
tion can be said to be effected formally explanatory.
when the following two conditions are met
(I.e., pp. 353-354). The first is connectabil- My tactic in this section is to evaluate
ways quite different from its other meanings in biological parlance (as in Nagel,
1961, p. 428 ff). Advantages to the above
approaches are already obvious simply from
their descriptions, but I will defer coalescing them into a summary until the penultimate section, after the issue of reduction
is dealt with formally. Those uninterested
in philosophical issues will not miss much
by going directly to that section.
COMMUNITY ECOLOGY
how closely Nagel's conditions might be
met by a mechanistic approach to community ecology. I will try to show that, while
of course community ecology has not been
reduced in toto, some prototype reductions
exist for certain of its aspects that appear
explicitly to satisfy Nagel's conditions in
most major ways. In so doing, I will be
trying out the notion that to the degree
Nagel's conditions are met by extant mechanistic approaches without excessive postulation of links and deductions not now
existing, and without excessive complexity,
the reduction is usefully explanatory. The
existence and success of such prototype
reductions, I will argue, makes a much
broader reduction at least plausible.
Levels of ecology
To attempt application of Nagel's conditions, we must first decide what the various subdisciplines of ecology are; each of
these will then be considered a separate
"science" in the above terminology. Moreover, as reduction proceeds from "higher"
to "lower" sciences in some sense (Medewar, 1974), we need to arrange the subdisciplines, inasmuch as possible, into a
hierarchy of levels. Hierarchies are perhaps best defined with respect to objects,
such as organisms or populations, which
are in fact the "parts" composing the various levels. I am going to assume in what
follows that these "parts" are the objects
of principal focus with respect to a particular subdiscipline, rather than being any
kind of object that is mentioned in the
phenomenology or theory of the subdiscipline. Hence, "term" is obviously a more
inclusive label than "part."
Beckner (1974) has stated the formal
conditions for a perfect hierarchy as follows:
(a) every part P; is assigned to exactly one
level L;;
(b) every part Pj (except those of the highest level) is a part of exactly one part
at each level above L(; and
(c) every part P; (except those of the lowest
level) is exhaustively composed of parts
at each level below L,.
I will now attempt to show that while a
91
perfect hierarchy exists for a subset of ecological subdisciplines, not all such subdisciplines are members of a perfect hierarchy. This is despite the fact that one of
the latter subdisciplines is often considered
to deal with the highest level of ecology.
The three disciplines of ecology that,
when narrowly enough constructed, do
form a perfect hierarchy, are individual
ecology (the parts are individuals), population ecology (the parts are single populations, defined as those individuals in some
place belonging to a single species) and
community ecology (the parts are collections of populations occurring in some
place). Individual ecology is itself decomposable into physiological, behavioral, and
functional-morphological ecology; all such
disciplines focus on the individual but have
somewhat different objectives and theoretical structures. Population ecology deals
with single species populations one-at-atime. Phenomena of interest include kinds
of items in the diet, behavioral and physiological thermoregulation, territory size
and mating strategies. The phenomena of
interest in population ecology are aggregate properties of the individuals composing the population; examples are age
structure, sex ratios, growth rates and
reproductive schedules. Community ecology deals with a group of populations in
some place. Here the aggregate properties
of interest concern the various species populations: abundance distributions, species
diversity, species-turnover rates, and soon.
These three kinds of ecology satisfy all of
conditions (a)-(c), at least ideally. That
condition (a) is satisfied follows from the
definition of the subdisciplines. That (c) is
satisfied follows from the definitions of
populations and communities. Condition
(b) is satisfied if communities comprise
mutually exclusive populations. Because
communities are in practice rather arbitrarily and dissynchronously defined, this
need not be the case, but one might imagine that a study by a consortium of
researchers would designate communities
in this fashion, and if the world's communities were ever catalogued, that they
would consists of non-overlapping populations.
92
THOMAS W. SCHOENER
A fourth subdiscipline of ecology, often
considered the highest level, is ecosystem
ecology. The parts are ecosystems; an ecosystem is denned as a community or communities plus the physical environment
(Whittaker, 1975). Once the physical environment is brought into the picture as a
part, condition (c), that each level is
exhaustively composed of parts from lower
levels, is clearly violated. One might try to
argue that physiological ecology also has
"parts," e.g., environmental input such as
solar radiation, that are purely physical; if
so, however, then individual ecology would
violate condition (b) and the perfect hierarchy would be destroyed from below. For
reasons expressed in the following paragraphs, I prefer not to think of physiological ecology this way, and I would argue
that ecosystem ecology is skewed aside from
the hierarchy from individuals to communities.
For the sake of completeness, a final subdiscipline of ecology is often recognized—
evolutionary ecology. It can be defined as
that portion of the larger science of evolutionary biology which is relevant to ecology. (One might simply recognize evolutionary biology as the "science" in question,
but I am trying to relate terms used here
to those commonly found in the literature.)
I see evolutionary ecology as arranged
almost entirely laterally with respect to
ecology's other subdisciplines, and certainly not in any perfect hierarchy. It is not
so clear what the "parts" of evolutionary
ecology are. If they are alleles and/or
genotypes, and if evolutionary ecology were
the lowest level, then condition (c) is violated. If its "parts" also include individuals
and populations, then condition (a) is violated, and either or both of conditions (b)
and (c) are also violated. Less formally, some
ecologists are interested in evolutionary
phenomena at various levels: the individual, the population, and the community
{e.g., Lewontin, 1970; Wilson, 1980). All
this may mean that evolutionary ecology is
better considered a "perspective" (in Wimsatt's [19766, p. 254] sense; for more on
identifying boundaries of sciences, see Dardenand Maull [1977]).
As mentioned, the rationale for propos-
ing a hierarchy is that typically reduction
is attempted from higher-level sciences to
lower-level sciences. However, the fact that
one science stands higher than another in
a perfect hierarchy is neither a necessary
nor sufficient condition for reduction of
the higher to the lower science. Levels are
defined with respect to parts; reduction is
defined with respect to theories. Hence
reduction may or may not be possible, given
a perfect hierarchy (Beckner, 1974). It is
also false to argue that if sciences are not
in a perfect hierarchy, one cannot be
reduced to another. One might be misled
by making the argument that parts in the
higher-level science may not be exhaustively composed of parts in the lower-level
science; therefore connectability is violated. But connectability refers to "terms,"
not "parts"; the former is a more inclusive
category than the latter, so that even if
there are "parts of parts" in a higher-level
science not occurring as parts in the lowerlevel science, they may still occur as terms
at the lower level, or at least be relatable
to terms at the lower level.
A related complication is that the theoretical structure of a particular ecological
subdiscipline may include as "terms" those
predominately used at higher levels, or even
include the "parts" of higher levels. For
example, in models of the costs and benefits of territorial defense, a major conceptual issue in individual ecology, the term
"rate of intrusion" is necessary. In turn
this term is strongly related to "number of
individuals in a population," a term of
major explanatory focus in population
ecology. But the fact that there exists
another subdiscipline of ecology devoted
to explaining such properties as population
number need not destroy the integrity of
lower-level subdisciplines using those terms:
In individual ecology, population number
might simply be considered an input
parameter in the same sense as radiant
energy or some other physical quantity
whose etiology is unnecessary for some kind
of explanation in that subdiscipline. On the
other hand, there might exist some "metatheory" spanning several disciplines such
that, for example, understanding population phenomena may contribute to predic-
COMMUNITY ECOLOGY
tive power at the individual level (see also
below). More generally, the subdisciplines
that we showed stand in a perfect hierarchy
on the basis of their parts may not have
entirely discrete bodies of theory. Moreover, if in some sense theories can be said
to be hierarchical, a perfect hierarchy
denned with respect to parts may not imply
the equivalent sort of hierarchy with
respect to theories or descriptions (Wimsatt, 1974). More on the relationship
between hierarchical phenomena and hierarchical theories is given below in the section on emergent properties.
In short, our analysis of levels has left
entirely open the question of the reducibility of ecology's subdisciplines. We now
attack that issue directly.
Formal reducibility of community ecology
This section considers the possible
reductive relationships between community ecology, population ecology and individual ecology. I will first attempt to show
that the population-dynamical approach to
modelling community-ecological phenomena makes a reduction of community to
population ecology plausible. I will then
attempt to show how much if not all of the
theoretical model-structure of population
ecology might be reduced to individual
ecology.
Community phenomena such as numbers of species and their abundance distributions can in principle be understood by
analyzing a set of differential (or difference, or hybrid) equations, each having the
abundance of a component species as the
dependent variable. For each such equation, independent variables may include
population abundances of other species on
the same trophic level and/or on different
trophic levels and physical quantities such
as the supply of some nutrient. The equations represent changes in abundances
through time, as these are affected by the
independent variables and parameters
through births, deaths, immigrations and
emigrations. Such equations in fact compose a large part of the theoretical machinery of the subdiscipline population ecology. Certain phenomena of interest there,
e.g., population growth, are directly rep-
93
resentable by such equations; others, such
as age structure, are representable by a
more extensive set of equations whose output can be combined to give changes in
total number of individuals in a population.
So commonplace is the use of such equations in community ecology that it is easy
to miss that this usage in fact may automatically constitute a reduction of that subdiscipline to population ecology. To be
convinced that this interpretation makes
sense, it is helpful to imagine theoretical
approaches in community ecology that do
not involve population-dynamical equations. A number of prominent ones exist,
including the early MacArthur (1960) broken-stick models for species abandances and
the MacArthur-Wilson (1967) theory of
island biogeography. The fact that the first
has been declared obsolete by its founder
(MacArthur, 1966) and the second by Williamson (1983) may indicate a general trend
of declining popularity of such models.
Although many, myself included, are far
from ready to write off the second as yet,
replacement of MacArthur-Wilson dynamics (where the dependent variable is number of species) with population-dynamical
models is certainly conceivable. All this
illustrates that the reduction of community
to population ecology is far from complete,
although it is plausible that it will eventually become entirely or nearly so.
A second difficulty for the just-proposed
course of reduction is that a theory having
to do with evolution in communities (e.g.,
character displacement) may not be representable using models whose variables are
of the kind listed above. Because the same
problem arises for the reduction of population ecology, I discuss the two together
below.
A much stronger kind of reduction of
community ecology to population ecology
would occur were there to exist no experimental laws or valid theoretical laws that
in any major way entail interactions
between species populations. Something
like this view has been favored by ecologists from Gleason (1926) to Simberloff
(1983; see Simberloff, 1980, for the weakest of disclaimers). The latter writes, for
94
THOMAS W. SCHOENER
number of existing systems, to make a statistically valid generalization.
I will next try to demonstrate that the
reduction of population to individual ecology is plausible and already has some prototypes among mechanistic approaches
that incorporate population dynamics. Approaches in the other class are in present
condition not suitable as they contain no
terms for births and deaths. They would
thus violate Nagel's condition of connectability unless suitable extensions were made
(in Schaffner's [1974] sense), in which case
they would converge to the populationdynamical approaches (see above). Moreover, I will argue that the consequent
formal transitivity from community to
individual ecology is sufficiently simple as
to result in a useful explanatory reduction
(sensu Wimsatt, 1976a) of community to
individual ecology.
The key condition to showing a reduction of a population-ecological theory to
an individual-ecological one is as follows.
Each parameter of the population-dynamical equations employed at the higher level
must be able to be translated completely
into individual-ecological (behavioral and
physiological) parameters. A single community-ecological parameter may correspond to one or more individual-ecological
parameters, and the latter can be arranged
in any functional form. It is this identification, I shall argue, that ultimately can be
A lot of the controversy in present-day interpreted as meeting Nagel's two critecommunity ecology can thus be viewed as ria.
about the strong reducibility of community
Let us assess this condition in detail. The
to population ecology, i.e., whether or not population-dynamical equations employed
species populations are additive or con- in the theory of population and community
junctive. If Simberloff and colleagues can ecology are often "descriptive," in the sense
show that species interactions are minor, that they are not derived but are chosen
then community ecology will cease to exist as the mathematically most convenient
as an interesting theoretical discipline. As expression that might in a very qualitative
is well known among ecologists, many per- sense embody the appropriate ecological
sons, myself included, strongly oppose the effect. Rarely are they chosen because they
view that anything like this has been shown have been shown in an extensive number
or even that recent research results are of cases to fit ecological data. Hence, they
headed in that direction (e.g., Schoener, should not be regarded as "experimental
1982; Roughgarden, 1983; Quinn and laws," in the sense such terms have in physDunham, 1983; Schoener, 1983; Connell, ics. An example of such an equation (for
1983; Paine, 1984). The only reason the population ecology) is the logistic equation
issue is not yet settled is that so little of single-species population growth. This
research has been done, relative to the equation is written
example, "we are asking if species' individual responses to the physical environment suffice to explain their distributions"
(Simberloff, 1983). If it can be shown that
neither vertical (e.g., predation) nor horizontal (e.g., competition) connections in the
food web are very important, then the ecology of single-species populations is sufficient to explain the phenomena of focus
in community ecology—species diversity,
relative abundances of species, species
turnover, and so on. (Notice that almost
certainly predation and competition will be
shown either both important or both unimportant, as they march in logical lock-step.
That is, significant resource competition at
one level implies significant predation at
the level of the resources and vice versa
[Hairston et al, I960].) In a very literal
sense the community then becomes a whole
which is entirely the sum of its parts, and
the limits that one places on the set of
species composing the community become
totally arbitrary. This does not mean that
community-level phenomena would necessarily disappear from ecological consideration. But it does mean that the theoretical explanation for such phenomena
would at best involve large-number concepts such as the central limit theorem—
e.g., the lognormal distribution of species
abundances results from many independent effects acting multiplicatively on independent populations (May, 1975).
COMMUNITY ECOLOGY
95
kind of derivation when carried out provides an explanation for an upper-level
generalization.
where N is population size, r is intrinsic
The preceding and similar derivations
rate of increase and K is carrying capacity. make explicit the common assumption of
This equation produces an S-shaped plot single-species equations that all individuals
of N vs. t, and the inflection point (point are identical. One cannot simply argue that
where dN/dt is at a maximum) is exactly we are considering the average individual,
at K/2. However, many real population- because mathematical expectations will
growth curves are not S-shaped; of those generally not go through, e.g., E(f(N)) #
that are, many have systematic deviations f(E(N)) (although they may approximately
of the inflection above or below K/2 do so). However, one can construct a sim(Schoener, 1973; Pomerantz et al., 1980). ilar equation for each intraspecific class of
Thus, despite the fact that certain authors individuals that is desirable to distinguish.
have treated the logistic curve as a "law" This will increase the complexity of the
(e.g., Pearl, 1925), it is not in fact even much model, perhaps dramatically, but the prinof a statistical law, to say nothing about its ciple remains the same.
failure as an absolute law.
Finally, note that N, the number (or denAlthough originally not derived in a sity) of individuals, is included in the dermechanistical way, the logistic equation can ivation. One might misleadingly argue that
be so derived. The details are elsewhere this is a population characteristic only, so
(Schoener, 1973) but the end result is writ- that the reduction is undermined. While
ten
N is a population characteristic, it is an
essential concept of behavioral ecology as
dN/dt = R[E(1 - AN*)
well—to understand the time and energy
- C - YN*]N
(3) budget of an individual, we need to know
where E = Te, \ = AT, and the symbols the density of other individuals. Terms
are defined as follows: R = number of indi- found at the population level may also be
viduals resulting from conversion of one found at the behavioral level and be vital
unit net energy input; e = net energy har- there.
vested by an individual feeding for unit
The logistic equation is the single-species
time; A = time spent by an individual in version of the Lotka-Volterra model,
interaction with a single other individual described above in connection with the ML
per unit time; T = proportion of total time protocol. A similar derivation (Schoener,
spent in activity (feeding or interaction); 19746) results in the parameter of the
C = cost of maintenance and replacement Lotka-Volterra model being written as a^ =
per unit time per individual (C = M + D F / (7y + EiXij)/(7ii + EiXji). Notice that in prinR, where M is metabolic rate and DF is the ciple there is nothing non-individual-ecoindividual death rate); y = energy cost of logical about terms describing interspecific
interaction with another individual per interactions; they are exactly the same type
individual per unit time; N* = N — 1. of term as those for intraspecific interacWhen rearranged, eq. 3 is exactly the logis- tions, so far as units are concerned and so
tic in terms of the variables. When the far as they would be measured in a behavapproximation is made that N* = N, the ioral study.
macroparameters of eq. 2 can be written
The preceding is certainly not the only
as r = R(E - C) and K = (E - C ) / ( T + way to derive a single-species populationEX). Each is decomposed into a combina- growth curve or one incorporating intertion of parameters having behavioral or specific competition. Other ways to derive
physiological meaning. (The one possible the logistic exist, each with a rather specific
exception is DF, which could conceivably domain of applicability. Moreover, the sort
be so decomposed [Schoener, 1973, 19746] of competition represented is entirely one
but has not been in this particular deri- type of interference; resources are assumed
vation.) As noted by Popper (1974), this "superabundant." Interestingly, when
(2)
96
THOMAS W. SCHOENER
resource-competition equations are derived
directly, they do not take on logistic form
but take on other, sometimes similar forms.
For example, a simple single-species equation in which the population is limited by
its resources is S-shaped but has its inflection between 0 and K/2 (as do many real
examples; for Drosophila, see Pomerantz et
al., 1980). This equation is written
dN/dt = RN[IE/(y + N) - C] (4)
where IE = bF 0 /T, y = l/(aT), and the
symbols not in eq. 3 are defined as follows:
b = the net caloric value of a unit of
resource; Fo = the amount of resource just
after the resource has renewed; T = the
renewal time of the resource; and a = the
consumption rate per unit resource of an
individual consumer. Here there is no term
corresponding to r; if K is taken to mean
"equilibrium population size," rather than
the specific parameter in eq. 2, then K =
( I E / Q - y.
Multispecies versions of resource competition are more complex and can take on
a variety of non-Lotka-Volterra forms
(Schoener, 19746, 1976, 1978; Abrams,
1980). Encouragingly, these forms (see
above) often fit the data better than do the
Lotka-Volterra equations, even when
number of free parameters is controlled
for.
Other examples similar to those just presented exist in the second group of papers
reviewed above. In some cases specific
equations are not used, but rather arguments about the convexity or slope of functions are used instead (e.g., part of Holt,
1983; Sih, 1984). Such arguments show
how a variety of mechanistically reasonable
equations conforming to a set of general
mathematical properties will produce a
given community-ecological prediction.
While the preceding sort of mechanistic
derivation has certainly not been done for
all equations used in population ecology, it
is easy to imagine that it could be done for
at least that subset which works reasonably
well. Put another way, it is plausible that
for every equation whose parameters are
not so decomposable, there exists an equation which is empirically indistinguishable
from the non-decomposable one and whose
parameters are decomposable into individual-ecological ones.
The above procedure, in which population-dynamical equations are derived
from mechanistic arguments about individual-ecological processes, simultaneously
provides connectability and derivability.
While the latter is not necessarily implied
by the former, it can be if for every term
A in the reduced science but not in the
reducing one there is a term B in the reducing science such that A and B are related
in a biconditional (Kemeny and Oppenheim, 1956, cited in Nagel, 1961). By
"biconditional" is meant the relation "A
if and only if B." Let us take as an example
of A the macroparameter K, where K is
given the broad definition of equilibrium
population size. The two derivations given
above produced two translations of K, one
for interference and the other for consumptive competition. So while we have
shown "K if B," where B is a particular set
of microparameters arranged in a particular form, we have not shown "K if only
B." In other words, there is a one-to-many
mapping of terms in the reduced science
to terms in the reducing science (e.g., Hull,
1976; Wimsatt, 1980a). Hence biconditionality would seem to fail. However, recall
that we are interested in explaining higher
level patterns in objects such as K. The two
derivations of K are of populations controlled by entirely different factors, one
the time and energy lost in encounter and
the other resource depletion. It is highly
likely that much more regular patterns will
be found for K's from populations of only
the first type, or from those of only the
second type, than from a mixture of the
two types. Thus our theoretical laws might
be most satisfactory when they treat the
two types of populations separately. If they
do, then K becomes almost a non-concept—one needs to specify which type of
K. Under such conditions, i.e., under a
pluralistic interpretation of K, biconditionality may indeed be satisfied. Even if it is
not, we still can show Nagel's two conditions, but I think that it is reasonable to
expect that an advanced theory of community and population ecology will satisfy
biconditionality much more than does the
COMMUNITY ECOLOGY
present one. Finally, I can think of no plausible many-to-one mappings between community or population ecology and individual ecology, at least given the statistical
nature of ecological laws. Many-to-one
mappings, were they to exist, would destroy
the formal reduction (e.g., Hull, 1976).
It is worth noting that the view just
expressed, that megaparameters having a
variety of derivations are likely to be fairly
useless in ecological theory, is nearly antithetical to Levins's (1966) advocacy of sufficient parameters in ecology. He writes
". . . temporal variation, patchiness of the
environment, productivity of the habitat,
and mode of hunting could all have similar
effects by way of their contributions to the
uncertainty of the environment. Thus
uncertainty emerges as a sufficient parameter." I am skeptical that this or other
parameters with so varied an etiology will
lead to much understanding in community
ecology, particularly given the great phenomenological variation that seems to exist.
Now let us examine the issue left dangling above, that a theory having to do with
evolution in populations (or communities)
involves evolutionary concepts not reducible to behavioral or physiological ecology.
There are two ways out of this quandary.
The first way is simply to define all evolutionary concepts out of community and
population ecology and into evolutionary
ecology. Indeed, a few ecologists are nervous with evolutionary ideas because of
their resistance to experimental test and so
would be quite happy with this banishment. However, assuming we retain the
evolutionary aspect of community and
population ecology, there is a second way
to defend their ultimate reducibility. That
is to show that community and population
ecology can be reduced to individual ecology and evolutionary ecology, taken jointly.
In other words, the evolutionary part of
the theory is embodied in equations with
evolutionary as well as populationaldynamical parameters and with evolutionary variables such as gene frequencies. I do
not have any complete examples of such a
reduction for a specific population- or community-ecological theory, but I see nothing
impossible about it in principle, especially
97
if decompositions of the populationdynamical parameters have already been
made. For example, elsewhere (Schoener,
1973) I have suggested how much decomposition of the population parameters r and
K can be used to resolve some of the controversy concerning for what traits r- and
K-selection, respectively, should be more
dominant.
One might also argue the reduction of
evolutionary versions somewhat differently, as follows. Behavioral and physiological ecology also have evolutionary
aspects. Hence "terms" already exist to
describe evolutionary concepts, so that an
expanded individual ecology that has evolutionary content will allow an evolutionary community or population ecology to
be reduced solely to individual ecology.
This then raises the question of what individual ecology might be reduced to—is
physiological ecology reducible to physics
and chemistry plus evolutionary ecology,
for example? One might also wonder
whether evolutionary ecology or evolutionary biology is reducible to lower sciences. I do not intend to deal with any of
these questions here, but I note that Ayala
(1968; see also Campbell, 1974) has argued
for the irreducibility of evolutionary biology. Briefly, Ayala's argument is that teleological concepts, by which he means concepts concerning adaptations and how the
latter are produced by natural selection by
way of increasing fitness in particular environments, are entirely absent from physics
and chemistry. This makes it inconceivable, according to Ayala, that Nagel's formal conditions could be satisfied.
One nasty technical flaw often frustrating attempts to satisfy Nagel's conditions
literally has been pointed out by a number
of persons (summary in Wimsatt, 1980a,
p. 216). This is the use of approximations.
Where such approximations are used, one
does not have perfect derivability in Nagel's
sense. A number of examples of such
approximations occur in the derivations of
eqs. 3 and 4 above; a function of exponential form was assumed the same as a rectangular hyperbola, for example. However,
not making the approximations would only
have incurred a modest increase in com-
98
THOMAS W. SCHOENER
plexity in the necessary equations. Probably this state of affairs would hold for all
approximations used in the mechanistic
approaches described above. That is, in
principle we can derive an equation exactly
corresponding to our mechanistic assumptions, but for ease of manipulation and,
consequently, the better to understand our
models, we use approximations. This issue
does, however, bring up the question of
feasibility, to which we return below.
Technical problems aside, is it conceivable that all of the interesting theory of
community and population ecology,
including evolutionary versions, could be
reduced in Nagel's or a more liberal sense
to individual ecology? One place where a
reduction must certainly fail, if it is to fail
at all, is with the complex of concepts associated with group selection. In certain
group-selection models, extinction of the
entire population depends, sometimes in a
threshold fashion, on population size. The
common scenarios are of a population that
overeats its food supply, attracts too many
predators, or (most plausibly) kills its host.
How can an ecology that deals entirely with
individuals reduce such a theory?
I think the ontological reduction (a la
Nagel or with Schaffner's [1974] extensions) could go forward as follows. Before
population size acts to exterminate all individuals in the population at once, the population is presumably behaving according
to the sorts of models discussed above, so
that such models tell us when critical population sizes are reached. Moreover, for
the most plausible scenario of group selection, populations of parasites, the individual ecology of the host, especially its physiological ecology, is crucial for predicting
actual values of critical parasite loads. Even
for the other group-selection scenarios
listed above (which to my knowledge have
never been shown to occur in nature—as
opposed to the laboratory, e.g., Wade
[1977]), the critical population sizes could
be predicted using a set of differential
equations representing food supply and
consumer populations or representing
predators and prey (e.g., Gilpin, 19756).
Such models, of course, could probably be
reduced in the sense illustrated by our pro-
totypes above. Hence, with a somewhat
more complicated set of links, the logical
empiricist reduction of Nagel is made plausible for ecological aspects of certain groupselection theory. Possibly the reduction
would be too involved to be very explanatory.
The preceding hypothetical scheme also
illustrates how reduction can serve to unify
subdisciplines in a very powerful way. As
stated above, individual ecology uses terms
from population ecology, but the former
discipline need not explain such terms to
have a valid and unique theoretical structure—the terms may simply be considered
as inputs, to be measured by the scientist
as soil types would be measured, but not
to be understood. However, having an
explanation for population size couched in
individual-ecological concepts not only
enriches population ecology but enriches
individual ecology as well. Thus, those
interested in feeding strategies can predict,
using population models, the changing
input feeding individuals have on populations of their food; those interested in
escape strategies can make similar predictions for predator populations. If such
changes are short-term enough, they
should in turn affect individual strategies
and how we predict them. The reduced
versions of population's and community
ecology will doubless fit more naturally into
such an expanded theory. One may or may
not then retain the separate designations
of the subdisciplines—the theoretical
structures of the fields would be greatly
unified, even though the respective phenomena must of course remain distinct.
In summary of this section, I have argued
that a non-evolutionary community ecology is in principle reducible to a non-evolutionary individual ecology via a reduction through population ecology, and that
an evolutionary community (and population) ecology is probably in principle reducible to either a non-evolutionary individual ecology plus evolutionary ecology or to
an evolutionary individual ecology by itself.
Furthermore, I have argued that for certain aspects of the upper-level theory, the
reduction is practical and/or has already
taken place. Notice that there is nothing
COMMUNITY ECOLOGY
but complexity barring the way from systems of equations with many variables being
reduced in the same way as the logistic
equation was reduced above. For example,
we could have many species in a food web
rather than a few, and we could be interested in modeling indirect effects (those
passing through intermediate species)
rather than direct ones—the reduction
could in principle still go forward.
Moreoever, the behavioral complexity
focused upon by the non-populationdynamical mechanistic approach could in
principle be incorporated into populationdynamical equations. Finally, when there
is one equation per species, the sometimes
unsatisfactory assumption that all individuals are equal could be taken care of by
replacing it with a set of equations for each
species, distinguishing (as is often done) age
classes, or size classes, or sexes. The resulting complexity, while not being a formal
impediment, could of course be a major
practical impediment, so that the reduction
would contribute little to understanding.
We shall return to this possibility below.
Emergent properties: A side issue
A major objection to my claims in the
previous section might be that I have
ignored emergent properties, which ecologists are discovering all the time {e.g.,
Richmond et al., 1975; Salt, 1979). The
existence of these emergent properties, it
might be argued, invalidates any reductionist demonstration, no matter how
apparently convincing.
In a completely formal, positivistic sense,
this argument is incorrect. As Nagel (1961)
points out, reduction does not deal with
properties; rather it deals with experimental and theoretical laws. One cannot
"deduce" properties per se; therefore it is
meaningless to talk about the irreducibility
of properties. Emergent, when applied
strictly to properties, is therefore not the
equivalent of Nagel's irreducible.
Hence, it may be better to speak of emergence with respect to relations between
statements (theoretical constructs) rather
than between properties. But we can still
ask the question, for community or pop-
99
ulation ecology: can those concepts associated with so-called emergent traits not be
explained by some population-dynamical
model whose parameters are ultimately
decomposable into individual-ecological
parameters? It is hard for me to imagine
they cannot. As Edson et al. (1981) point
out (in reply to Salt, 1979), just because a
relationship is not additive between components does not mean it is qualitatively
different from additive ones (also see Hempel and Oppenheim, 1948). In particular,
relationships between individuals or populations derived from individual-ecological
principles will seldom be linear. To take
the simplest of examples, if a certain
amount of resource I is to be divided among
N individuals, then each individual gets I/N
resource, assuming no limitations on an
individual's resource-consumption abilities. Where limitation does exist, each individual's share can be represented as I/(N +
x) (Schoener, 1978). These are already
highly nonlinear terms. To rephrase, the
fact that the whole is not the sum of its
parts by itself does not rule out reducibility, and if the whole were the sum of its
parts, we would have an especially strong
form of reductionism, as discussed above.
Labelling properties "emergent" which
show nonlinear "composition functions"
(in Hempel and Oppenheim's [1948] sense)
could not be taken to imply that theories
associated with those properties are irreducible, so this use of the term may be illadvised, at least without clearly distinguishing which sense of the term is meant
(e.g., Wimsatt, 1976&). Moreover, for population and community ecology, where
nonlinearity is the rule, this distinction
would not divide such functions into anything like two equally sized groups.
An interesting subconcept, which
attempts to isolate that portion of a broad
concept of emergence that is unique, is
Beckner's (1974) "event reduction." It is
denned as follows. Let T h be a theory of a
higher-level science and T, be a theory of
a lower-level science. Then T h is subject to
complete (restricted) event reduction to T,
if and only if every event which exhibits
the phenomenon described by T h is explicable in T,, under every (some) description
100
THOMAS W. SCHOENER
of the event in the vocabulary of T h . An
"event" is most generally a particular
occurrence or happening. For community
or population ecology, an "event" might
be taken to mean the occurrence of a process, or part of a process, in some ecological
community, e.g., the change in population
size of a component species.
Complete event reduction is stronger
than mere reduction, a la Nagel. If Simberloff and colleagues are correct about
species interactions being so weak in nature
as to be ignorable, then complete event
reduction of community to population
ecology would almost certainly go through
under the revised theory that would then
exist. As pointed out, many ecologists do
not believe this hypothesis about communities to be correct. Nonetheless, as I elaborate below, event reduction can be a useful concept for community ecology so far
as research strategies are concerned.
T H E MECHANISTIC APPROACH AS A
RESEARCH STRATEGY
Wimsatt (1980a) has written that "the
in principle claim of the reductionist is seldom in dispute," and that in the fields he
is familiar with, "the issue between scientists who are reductionists and holists is not
over the in principle possibility of an analysis in lower-level terms but on the complexity and scope of the properties and
analyses required." Whether this is true or
false for community ecology (I doubt community ecologists have thought much about
it until recently), Wimsatt is certainly correct that, given that reduction is in principle possible, its execution may not be
worth the trouble in terms of insights
gained or research facilitated. Toward
evaluating this possibility, I now discuss the
pros and cons of the mechanistic approach
to community ecology as (to use Wimsatt's
phrase) a "research strategy."
Many advantages of the mechanistic
approach have been mentioned above and
discussed in other papers in this symposium. For myself, its chief advantage is that
it allows a theoretical understanding of how
variation in individual-ecological properties—those of behavioral and physiological
ecology—affect population and commu-
nity structure. As Wimsatt (1976a) points
out, this would not be so vital were there
few exceptions to laws at the macrolevel,
or were exceptions homogeneous when
translated into microlevel terms. In fact,
variation at the community (and population) level is extensive, so much so that
rather than exceptions to a few "laws," it
appears that community ecology is a genuinely pluralistic field, with many different
"laws," each restricted to a rather narrow
domain (Schoener, 1985).
There are at least three major consequences of the advantage just discussed.
First, as illustrated in the theoretical
papers of Schoener (1973, 1974, 1976,
1978), Abrams (1980, 1982), Holt (1983)
and Sih (1984) discussed above, qualitative
predictions about how behavioral and
physiological properties affect community
and population dynamics and equilibrium
become possible. For example, does an
energy-maximizing predator stabilize or
destabilize a predator-prey relationship?
What behavioral traits would result in a
population with a leptokurtic utilization
distribution? How does metabolic rate
affect population growth rate and stability
of species interactions?
Second, from an array of possible submodels available for a community (or ecosystem) model, the mechanistic approach
suggests which to select. The most appropriate model would not be an issue were
all models with roughly similar qualitative
properties to behave the same way. But it
is becoming obvious that in some major
cases, and perhaps in many, they do not.
Two relevant examples from community
ecology stand out. In the first, Gilpin and
Justice (1972) showed that, depending upon
whether the zero-isocline of a competition
model were linear (Lotka-Volterra) or concave, two qualitatively opposite predictions
would be made about the outcome of competition in an actual system, two species of
Drosophila. In fact, the isoclines were in
reality concave, and the Lotka-Volterra
model gave the incorrect prediction. At a
more general level, Turelli (1981) showed
that which of three qualitatively similar
population-growth functions were used in
a stochastic model determined the degree
COMMUNITY ECOLOGY
and direction of the effect of environmental
variation on community stability: one gave
a positive effect, one a negative effect, and
one no effect! Ecosystem ecologists are also
becoming aware of the problem. After citing some examples, Watt (1975), in a spate
of disillusionment with ecosystem modelling, wrote
"What is the meaning of the phrase 'a
general function which describes this
curve is . . . ?' Does it mean that the function was plucked out of thin air as being
reasonable, or that it was tested against
various sorts of ecological data to ensure
that it described reality reasonably well,
or that it was the product of some type
of deductive process which will be outlined at some later time so as to be completely intelligible? Particularly where the
function is new in ecological writings,
and the explanation for its origin is not
given, the critic is basically trapped in a
guessing game with the author" (p. 140).
My suspicion, as also voiced above, is that
the first of Watt's alternatives is almost
always true, and unfortunately, it appears
that this may be no longer good enough.
The above examples make me less than
optimistic, contra Levins (1966; see also
Wimsatt, 19806), about the robustness of
community-ecological models, even
regarding small details, much less at the
scale he is talking about.
If choice of model or submodel makes a
difference, how is one to choose? The
answer may well lie with the mechanistic
approach: select an appropriate mechanistically derived model, rather than one
that is arbitrary or at best purely descriptive. And as a coda, do not hesitate to
change models or submodels when the situation changes.
The third consequence is perhaps the
most ambitious in its claims, but some outstanding examples of its success exist. It is
that the mechanistic approach allows quantitative predictions to be made about community structure from behavioral and
physiological considerations which can be
tested with independently gathered
macrolevel data. Four studies illustrating
this advantage were described above and
101
are briefly reiterated here. First, Belovsky
(1984, 1986) fitted by non-linear regression population data describing competition between moose and hare. The "bestfit" population parameters were then
translated into behavioral parameters, via
a mechanistic model, and those estimates
were compared with independently derived
estimates of the behavioral parameters
obtained from behavioral-ecological (feeding-strategy) considerations. The two were
found to be very close, greatly increasing
our confidence in the theory. Second, Tilman (Titman, 1976;Tilman, 1977, 19866)
used Michaelis-Menten growth considerations to predict quantitatively the values
of nutrient ratios that determine different
kinds of competitive outcomes. These predictions were verified with experiments.
Third, Abrams (1981a) checked his estimates of a competition "coefficient"
obtained from a model of shell dynamics
with observations of marked shells in the
field. Again, the two were very close.
Fourth, Spiller (1985) evaluated a mechanistic competition-coefficient formula with
field observations, then performed field
experiments to measure the coefficient
directly. Again, agreement was very good.
All four of these studies are extremely
powerful, in that they allow two independent assessments of a community-ecological theory. When the two are in agreement,
our confidence in the theory is greatly
increased.
One might wonder from the rosy picture
I have just painted why ecologists have not
all boarded the mechanistic bandwagon. I
think the basic caution of the dissenters is
that this approach may portend an extraordinary degree of complexity when manyspecies interactions are considered. The
complexity could arise in two kinds of places
in the theory. First, any particular model,
if it is to incorporate enough behavioral or
physiological variation, may have to be so
complex as to be analytically opaque.
Already, a tradeoff in this area is detectable
within the mechanists themselves. Those
who delete population dynamics from their
approach can incorporate more behavioral
variation than those who do not (see above).
Second, even if individual models are man-
102
THOMAS W. SCHOENER
ageable, too many models, each with a very
narrow application, may render the entire
theory so massive and arcane that community ecology will become an impossibly
esoteric field, unteachable to undergraduates and run mainly by experts in information retrieval. Worse, a theory too composed of special cases may be untestable,
at least without intergalactic travel, as the
earth may not contain sufficient communities to provide adequate statistical power.
Some ecologists are probably willing to
give up a lot of precision and linkage to
lower levels if these things can be avoided.
Moreover, as Wimsatt (1980a) points out,
advocacy of a reductionist approach coincides with emphasizing internal, rather
than external, factors when simplification
is necessary. Thus mechanistic people will
stress behavioral and physiological detail
at the expense of, say, food-web detail. Twospecies systems rather than many-species
systems, and direct rather than indirect
effects, will be emphasized. This is already
to some extent happening (see contrasts in
Diamond and Case [1985] for example).
As pointed out above, it is not that in
principle the mechanistic approach is
unable to handle phenomena involving
numerous population- or community-level
variables, e.g., numerous species. It is just
that in practice, this may be too overwhelming. The hope that computer technology can make any "in principle" actual
is dashed by reading Boyd (1972) and Wimsatt (1980a). For example (Wimsatt, 1980a),
there are approximately 10130 possible chess
games of 100 moves, larger by about 41
orders of magnitude than the number of
elementary particles in the universe and by
about 8 orders of magnitude of the number of physical events between such particles since the "big bang." So there have
not been enough actual states to represent
the chess game even if the universe since
its inception were a computer! Those who
have ever contemplated a very microreductionist approach to community ecology, e.g., following the fate of each individual (rather than representative individuals)
in a set of interactions potentially very much
more complicated than chess should be
sobered by these calculations. That which
is in principle possible may in fact not be
physically possible. The mechanistic people, of course, are not advocating such an
approach, and their hope is that reduction
(in the way I have described it) may actually
sometimes lead to meaningful simplification, not greater complexity.
Even for the same degree of complexity
(as measured, say, by the number of free
parameters in a model), the descriptive
approach may be more suitable than the
mechanistic one if description is an end in
itself. That is, it is conceivable that the most
descriptive model for a particular case is
nonmechanistic, or more likely, that the
single model describing a set of cases better
than any other is nonmechanistic. Because,
as stressed above, ecological phenomena in
toto rarely fit any single model well, the
latter is in my opinion not so likely; an
example is found in my own work on habitat shift (Schoener, 1974&). This is also
why the role of upper level generalizations
in "winnowing out" inappropriate lower
level representations (as suggested by
Wimsatt, 1976&, footnote 11) is not likely
to be conspicuous for population and community ecology even if it were looked for
carefully.
A related advantage for nonmechanistic
models, especially linear ones, is their typically intimate association with statistical
estimation. Again, however, estimation is
possible with nonlinear models; it is just
more cumbersome. Moreover, if the
assumptions of the estimation {e.g., linearity) are far from true, reliability of the estimation is compromised, and a more complicated estimation procedure (or no
procedure) may be preferable.
Finally, of course, reduction has to stop
somewhere along its downward path. While
I have argued that it may often be practical
to reduce community and population ecology to individual ecology, would it be sensible to go farther? That is, should we use
physiological laws such as the metabolicrate-to-body-weight function in their simple descriptive form, or should we use a
probably more complicated mechanistic
version were one available? And if the
answer is yes, should we continue through
biochemistry, physical chemistry and phys-
COMMUNITY ECOLOGY
ics? If this reductio ad absurdum (or ad nauseum\) were possible in principle, it would
be strangulating in practice. Scientists will
place bounds on a train of reductions that
are in principle possible when the sequence
becomes too long to have explanatory
power (Wimsatt, 1976ft).
Despite occasional bursts of ambitious
pronouncement, we are not going to know
for a very long time how the balance of
advantages and disadvantages will finally
fall. But it is amusing, not very risky, and
perhaps even a bit inspirational to speculate, which I now do.
A MECHANISTIC ECOLOGIST'S UTOPIA
What if the mechanistic program realized its wildest aspirations? What would
ecology be like then? Here I imagine the
characteristics of a mechanistic community-ecologist's Utopia. I distinguish six such
characteristics.
First, the macroparameters of community ecology will be de-emphasized. Less
use will be found for concepts like "niche
overlap," "niche breadth," and indeed even
"niche." "Niche overlap," for example,
might be represented by an array of more
specific concepts, such as Abrams's (1980ft)
competition ratio, Schoener's (1974a)
competition coefficient, and so on.
Second, theoretical models will have
proliferated, and each will have a rather
specific domain. A pluralistic theory will
have replaced an attempted universal one.
Pluralism will involve specificity at both the
organismic and environmental levels, i.e.,
with respect to the biological traits of the
type of organism being considered (e.g.,
generation time) and the environmental
traits of the community's location (e.g.,
degree of spatial fragmentation). Elsewhere (Schoener, 1985a), I have suggested
a first list of such traits.
Third, arbitrary models whose sole virtue is mathematical convenience will no
longer be acceptable. In order to be used,
a model will have to be mechanistically justifiable. It may be that manipulation of such
models will require a great deal of mathematical skill with approximations and so
on, and perhaps a lot of computer time as
well.
103
Fourth, in both observational and experimental approaches, a greater emphasis will
be placed on discovering the mechanism
of an interaction or process, not just its
existence and strength. The ingenuity
required to get at such mechanisms will
probably be much greater than that needed
to design the removal or introduction
experiments that most of us do today.
Fifth, individual-ecological terms, e.g.,
those from behavioral and physiological
ecology, will commonly appear in designations of kinds of ecological communities.
Thus we might have ectothermic communities, semelparous communities, or
long-generationed communities.
Sixth, population- and community-level
hypotheses will be framed in much more
precise and obviously testable terms than
is presently the case. Perhaps Beckner's
(1974) application strategy involving event
reduction (see above) will be realized: the
"revision of higher-level theory in a manner that facilitates event reduction; that is,
the introduction of higher-level descriptions with an eye toward the lower-level
explanation of events under those descriptions." The use of quantities and units from
behavioral and physiological ecology may
bring testability of population- and community-ecological models on a par with that
currently possible for, say, feeding-strategy models (e.g., Krebs et ai, 1983).
Notice that nothing in this scenario suggests replacement of community ecology by
individual ecology as a science, despite the
prospect of reduction. The phenomena of
community ecology will still be of interest
(unless the Florida State proposal is correct), although as stressed above, there will
be a good deal more unity between the
subdisciplines than presently exists. (In this
regard, I am supporting Wimsatt's [1976ft,
p. 222] view of "interlevel" reduction.)
Of course, as already noted, actualization of the mechanistic program could well
falter on complexity and unwieldiness.
Exactly what will happen remains to be
seen, but we may ask in closing about the
effect this and other philosophical analyses
might have on the development of community ecology. Will a philosophically selfaware science pursue a different path than
104
THOMAS W. SCHOENER
one that is philosophically ignorant? Philosophers are sometimes surprisingly selfeffacing on this question (e.g., Beckner,
1974), and in fact it is probably unanswerable; we are participants in an experiment
without a control.
ACKNOWLEDGMENTS
I thank P. Abrams, M. Price, J. Quinn,
J. Roughgarden, A. Shapiro and D. Spiller
for comments on a previous draft. I am
especially indebted to J. Griesemer for
pointing out much of the relevant philosophical literature to me and for very
detailed criticism and discussion. Supported by N.S.F. grant DEB 81-18920.
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