Write a 750- to 1,250-word paper in which you complete one of the following opti

Question

Write a 750- to 1,250-word paper in which you complete one of the following options:

Option 1: Food Webs Case Study

Explain the theory in your own words based on the case study and suggested readings.

Include the following in your explanation:

Competition

Food Webs

Boxicity

Trophic Status

Give an example of how this could be applied in other real-world applications.

Format your paper according to APA guidelines. All work must be properly cited and referenced.

Submit your assignment to the Assignment Files tab.

PART III
GRAPH THEORY
224
13
Food Webs
Author: Robert A. McGuigan, Department of Mathematics, Westfield State
College.
Prerequisites: The prerequisites for this chapter are basic concepts of graph
theory. See Sections 9.1 and 9.2 of Discrete Mathematics and Its Applications.
Introduction
A food web is a directed graph modeling the predator-prey relationship in an
ecological community. We will use this directed graph to study the question of
the minimum number of parameters needed to describe ecological competition.
For this purpose we will consider how graphs can be represented as intersection
graphs of families of sets.
We will also investigate the axiomatic description of measures of status in
food webs.
Competition
In an ecological system, the various species of plants and animals occupy niches
defined by the availability of resources. The resources might be defined in terms
of factors such as temperature, moisture, degree of acidity, amounts of nutrients,
225
226 Applications of Discrete Mathematics
and so on.
These factors are subject to constraints such as temperature lying in a
certain range, pH lying within certain limits, etc. The combination of all these
constraints for a species then defines a region in n-dimensional Euclidean space,
where n is the number of factors. We can call this region the ecological niche
of the species in question.
For example, suppose we restrict ourselves to three factors, such as temperature,
nutrients, and pH. Assume that the temperature must be between t1
and t2 degrees, the amount of nutrients between n1 and n2 and the pH between
a1 and a2. Then the ecological niche these define occupies the region of
3-dimensional Euclidean space shown in Figure 1.
Figure 1. An ecological niche.
Euclidean space which has as dimensions the various factors of temperature,
pH, etc., is called an ecological phase space. Generally, no two distinct
species will have the same ecological niche in phase space; however, two species
compete if their ecological niches have non-empty intersection. A basic principle
of ecology, known as the principle of competitive exclusion, dictates
that species whose niches are too similar, or overlap too much, cannot coexist.
If the factors defining the niche are independent, then the niche in phase space
would be a box such as that in Figure 1. If the factors are not independent,
i.e. the level of one depends on levels of others, then the niche would be some
other type of set, e.g. convex, but not a box.
For example, consider the two factors temperature (t) and per cent humidity
(h). We might have constraints such as: t must be between 0 and 100, and h
must be between 0 and 100t t2. In this case temperature and humidity are
not independent; the possible values of h depend on the values of t. The region
in two-dimensional space defined by these constraints is not a rectangle.
Our discussion of ecological communities and related concepts such as
Chapter 13 Food Webs 227
species, food webs, and competition will be somewhat oversimplified in order
to make a brief presentation possible. Interested readers should consult reference
[1] for an in-depth treatment of these topics. Our mathematical treatment
follows that of reference [6].
Food Webs
It may be difficult to know all the factors which determine an ecological niche,
and some factors may be relatively unimportant. Hence it is useful to start with
the concept of competition and try to find the minimum number of dimensions
necessary for a phase space in which competition can be represented by niche
overlap.
One approach to this question is to consider the notion of the food web of
an ecological community.
Definition 1 A food web of an ecological community is a directed graph
with a vertex for each species in the community and a directed edge from the
vertex representing species A to the vertex representing species B if and only
if A preys on B.
Figure 2 shows a simple food web for a community of seven species: robin,
fox, grasshopper, raccoon, salamander, milksnake, and toad.
Figure 2. A simple food web.
We can define competition using the food web. Two species compete if and
only if they have a common prey. Thus, in the example of Figure 2, raccoon and
fox compete (since robin is a common prey), milksnake and raccoon compete,
228 Applications of Discrete Mathematics
while salamander and robin do not compete. We use this competition relation
to define a graph called the competition graph.
Definition 2 The competition graph of a food web is a simple graph with a
vertex for each species. Two vertices are joined by an (undirected) edge if and
only if the species they represent have a common prey.
Example 1 Find the competition graph for the food web of Figure 2.
Solution: The competition graph for this food web is shown in Figure 3.
Figure 3. A competition graph.
To represent the competition relation in phase space we want to assign to
each vertex of the competition graph a subset of Euclidean space of some dimension
in such a way that two vertices are joined by an edge in the competition
graph if and only if the sets assigned to these vertices have non-empty intersection.
Figure 4 shows a representation of the competition graph of Figure 3,
using an interval for each vertex. We have thus represented the competition
graph using only one dimension.
Figure 4. Interval representation of a competition graph.
We can now state a general mathematical problem, but first we need to
develop some terminology.
Chapter 13 Food Webs 229
Definition 3 A graph is an intersection graph for a family of sets if each
vertex is assigned a set in such a way that two vertices are joined by an edge if
and only if the corresponding sets have non-empty intersection.
Definition 4 A graph is called an interval graph if it is the intersection
graph for a family of closed intervals.
Our goal is the representation of competition graphs of families of sets
in Euclidean n-space. Clearly the simplest case would be that of competition
graphs that are interval graphs. This would mean that only one ecological
factor is necessary to describe niche overlap.
Example 2 Find the interval graph for the family of closed intervals A =
[1, 3], B = [2, 6], C = [5, 8], D = [4, 5].
Solution: We use the definition of intersection graph to obtain the graph of
Figure 5.
Figure 5. An intersection graph.
Example 3 Prove that the 4-cycle graph C4 of Figure 6 is not an interval
graph.
Solution: The proof depends on the order properties of the real numbers. Let
the interval corresponding to vertex n be [nl, nr]. Since the intervals for vertices
1 and 2 overlap, we must have either 1l 2l 1r 2r or 2l 1l 2r 1r,
Assume for specificity that 1l 2l 1r 2r. The argument for the other case
is analogous.
Since the interval for vertex 3 must meet that for vertex 2 and must not
meet that for vertex 1, we must have 1l 2l 1r < 3l 2r. Now the interval
for vertex 4 must meet those for both vertices 1 and 3, so we have to have
1l 4l 1r and 3l 4r 3r since interval 1 lies entirely to the left of interval
3. However, since 2l 1r < 3l 2r, the intervals for vertices 2 and 4 overlap,
which is forbidden.
230 Applications of Discrete Mathematics
Figure 6. A graph that is not an interval graph.
The 4-cycle can, however, be represented as the intersection graph of a
family of boxes in Euclidean 2-space, as shown in Figure 7.
There are several methods known for determining whether a simple graph
is an interval graph. A detailed discussion of this topic may be found in Roberts’
book [6]. We simply state the characterization due to Gilmore and Hoffman
[3] without proof. Before the characterization can be stated, we need some
definitions.
Figure 7. A box representation.
Definition 5 A graph H is a generated subgraph of a graph G if the vertices
of H are a subset of the vertices of G and vertices in H are adjacent in H if
and only if they are adjacent in G.
Definition 6 The complement of a graph G is the graph G where the vertices
of G are the vertices of G, and two vertices in G are adjacent if and only if they
are not adjacent in G.
Definition 7 An orientation of a graph G is an assignment of a direction to
each edge in G (which makes G into a directed graph).
An orientation is transitive if whenever (u, v) and (v,w) are directed edges,
then (u,w) is a directed edge.
Chapter 13 Food Webs 231
The characterization due to Gilmore and Hoffman is given by the following
theorem.
Theorem 1 A graph G is an interval graph if and only if it satisfies the
following two conditions:
(i) The four-cycle C4 is not a generated subgraph of G,
(ii) The complement of G is transitively orientable.
Our goal in our study of ecological competition is the representation of
niches in Euclidean space and competition by niche overlap. It seems desirable
in an ideal representation that the factors determining the dimension of the ecological
phase space would be independent and the niches would be represented
as “boxes”, or Cartesian products of intervals. This leads us to the next part
of this discussion, namely, when can we represent a graph as the intersection
graph of a family of boxes in n-space.
Boxicity
Definition 8 The boxicity of a graph G is the smallest n such that G is the
intersection graph of a family of boxes in Euclidean n-space.
Note that an interval graph is simply a graph with boxicity equal to 1.
It is not entirely clear that every simple graph has a boxicity. The following
theorem resolves this difficulty.
Theorem 2 Every graph G with n vertices is the intersection graph of a
family of boxes in Euclidean n-space.
Proof: Let v1, v2, . . . , vn be the vertices of G. A box in Euclidean n-dimensional
space is the set of all n-tuples of real numbers (x1, x2, . . . , xn) such that
each xi is in some closed interval Ii. Now, for each k = 1. . . , n and each vertex
vi, define closed intervals Ik(vi) as follows.
Ik(vi) =


[0, 1] if i = k
[1, 2] if i    = k and {vi, vk} is an edge in G
[2, 3] if i    = k and {vi, vk} is not an edge in G.
For each vertex vi define a box B(vi) in Euclidean n-space by
B(vi) = {(x1, x2, . . . , xn) | xj Ij(vi) for j = 1, . . . , n}.
232 Applications of Discrete Mathematics
Thus, the box B(vi) corresponding to vi is the Cartesian product of the intervals
Ij(vi) for j = 1, . . . , n.
Now we show that vi and vj are adjacent in G if and only if B(vi)B(vj )    =
. Thus the graph G is the intersection graph of the family of boxes B(vi). First,
suppose that there is an edge joining vl and vm. If k is different from both l and
m, then according to the definition, Ik(vl) Ik(vm) is [1, 2] [1, 2], [1, 2] [2, 3],
or [2, 3] [2, 3]. In any case we have Ik(vl) Ik(vm)    = . If k=l or k=m then
Ik(vl) Ik(vm) = [1, 2] [0, 1]    = . So, if there is an edge joining ve and vm,
then for all k, Ik(vl) Ik(vm)    = . Hence B(vl) B(vm)    = .
Now suppose that B(vl)B(vm)    = . Then for each k from l to n, Ik(vl)
Ik(vm)    = . Set k = l then Il(vl) = [0, 1] and Il(vm) must be [1, 2] for the
intersection to be nonempty. By definition of Il(vm), vl and vm are adjacent.
Thus G is the intersection graph of the family of boxes B(vi).
This theorem shows that boxicity is well-defined. Unfortunately, there is
no efficient algorithm known for determining the boxicity of a general graph.
There is no characterization known for graphs of any specific boxicity other
than 1.
In fact, there are not many general classes of graphs for which the boxicity
is known. It is not hard to see that the boxicity of the n-cycle Cn is 2 for n = 4
or larger, and this is left as Exercise 6. Another general class of graphs for which
the boxicity is known is the complete p-partite graphs. These are the graphs
Kn1,n2,...,np defined as follows: there are n1 + · · · + np vertices partitioned into
p classes, where the ith class has ni vertices. Within a class no vertices are
adjacent, and every vertex in any class is adjacent to all vertices in the other
classes. Roberts [6] showed that the boxicity of Kn1,...,np is equal to the number
of ni that are larger than 1.
One result which helps somewhat in calculating the boxicity of a graph is
due to Gabai [2]. This theorem depends on the concept of independence of a
set of edges.
Definition 9 A set of edges in a graph is independent if they have no vertices
in common.
Gabai’s theorem [2] is the following, stated without proof.
Theorem3 Let G be a simple graph. If the maximum size of an independent
set of edges of G is k, then G has boxicity less than or equal to k. Also, if G
has a generated subgraph consisting of k independent edges then the boxicity
of G is greater than or equal to k.
Chapter 13 Food Webs 233
Gabai’s theorem is useful in determining the boxicity of relatively small
graphs and for certain families. In any case it limits the amount of trial and
error needed.
In our study of competition we search for the representation of the competition
graph of a food web as the intersection graph of a family of sets in
Euclidean n-space for some n. As a consequence of the theorem proved above,
this representation is always possible. Furthermore, we can use the boxicity
of the competition graph as an indicator of the minimum number of factors
essential for describing competition in the community. Cohen [1] has studied
more than 30 single-habitat food webs published in the ecological literature and
has found that the competition graphs of all of them are interval graphs. That
is, in all cases one dimension suffices to represent competition by niche overlap.
It is not known whether this is a general law of ecology, but it does raise many
interesting questions. In some single-habitat communities a single dimension
for the niche space can be identified. It may be some obviously linear factor
such as temperature, body length or depth in water. However, it may well be
that more than one single dimension will work. And, of course, we can’t expect
the single-niche dimension to be the same from community to community.
Hypothetical food webs have been constructed such that their competition
graphs are not interval graphs, but these combinations of species have never
been observed in nature at the same time and place.
The representation of graphs as intersection graphs of boxes has important
applications in ecology, as we have seen. Applications to such diverse fields
as archaeology and automobile traffic control have also been investigated (see
reference [6]). We conclude with an additional application of food webs.
Trophic Status
In the study of social systems it is often useful to measure the status of an
individual in an organization. Harary [4] first introduced the idea of measuring
the status of a species in a food web. In ecology this status is usually called the
trophic level and is helpful in assessing the complexity and diversity of a web.
The idea is that a web with many species at each trophic level has a high degree
of complexity. In ecology it is generally thought that more complex ecosystems
are more stable. In this section we study the question of how trophic status
can be defined in a food web.
If the food web is simply a directed path (a food chain) then it is easy to
define trophic status; just follow the order of the species in the chain. Some
other structures also allow for an easy definition of trophic status. For example,
we might think of species with no outgoing edges as being at the bottom of the
web. Suppose that for every vertex, all directed paths to vertices at the bottom
have the same length. Examples of such webs are given in Figure 8.
234 Applications of Discrete Mathematics
Figure 8. Graphs of two food webs.
In this kind of web, the trophic status of a vertex can be defined as the
length of a directed path from the vertex to the bottom.
In general it is difficult to define trophic status in complicated food webs.
Because more than one approach may be possible, we will use the term trophic
status in this context rather than the term trophic level which is well-known in
the context of food chains. Our goal is to investigate how trophic status could
be measured rather than to develop a unique possibility.
To start, we need some basic assumptions about food webs. In particular,
we assume that our food web is acyclic, i.e. that the directed graph has no
cycles. Thus, there are no species s1, . . . , sn such that for i = 1, . . . , n 1, si
preys on si+1 and sn preys on s1. In particular there are no two species such
that each preys on the other. Thus, the prey relationship is asymmetric.
We will take an axiomatic approach to defining measures of trophic status.
That is, we will state conditions which any reasonable measure should satisfy in
the form of axioms. A measure will then be acceptable if and only if it satisfies
the axioms. The axioms will define an ideal model for the concept of measure
of trophic status. Our approach will follow that of Harary [4] and Kemeny
and Snell [5], who work with status in an organization, and the treatment in
Roberts [6], which is more detailed.
Definition 10 In a food web a species v is a direct prey of a species u if
there is a directed edge from u to v. A species v is an indirect prey of u if there
is a directed path from u to v.
It could well happen that there are two species u and v neither of which is
an indirect prey of the other.
Definition 11 If v is a direct or indirect prey of u, then the level of v relative
to u is the length of the shortest directed path from u to v.
Chapter 13 Food Webs 235
We can now state some reasonable axioms for measures of trophic status.
Let tW(u) be the measure of status in the food web W. The axioms are:
Axiom 1: If a species u has no prey then tW(u) = 0.
Axiom 2: If, without otherwise changing the food web, we add a
new vertex which is a direct prey of u to get a new web W, then
tW (u) > tW(u).
Axiom 3: Suppose the web W is changed by adding edges and/or
vertices in such a way that the level of some direct or indirect prey of
u is increased, and no direct or indirect prey of u has its level relative
to u decreased. If W is the new web, then tW (u) > tW(u).
These axioms make sense intuitively when we consider that we are saying that a
species with no prey is at the bottom level (Axiom 1), that if the number of prey
of a species increases its status increases (Axiom 2), and that the status of a
species increases if its level relative to some indirect prey is increased (Axiom 3).
There is a measure of status which satisfies the axioms. Harary [4] suggested
the following definition.
Definition 12 If a species u has nk species at level k relative to u for each
k, then
hW(u) =

k
knk.
Theorem 4 The measure hW(u) satisfies Axioms 1-3.
Proof: If u has no prey, then hW(u) = 0 because all the nk = 0.
If we add a direct prey for u, then n1 increases by 1, so the sum defining
hW(u) also increases.
Likewise, if some direct or indirect prey of u at level k relative to u is
moved to level k + n below u and no other direct or indirect prey of u has its
level decreased, the sum for h increases by at least kn, verifying Axiom 3.
Kemeny and Snell [5] also show that if tW is any other measure of trophic
status satisfying Axioms 1–3 and having all its values nonnegative, then for all
species u, tW(u) hW(u). Thus, h is in a sense a minimal measure of trophic
status.
While h satisfies our axioms it fails to have other desirable properties. For
example, it seems reasonable that if tW is a measure of trophic status and v is
a direct or indirect prey of u, then
tW(u) tW(v).
236 Applications of Discrete Mathematics
The measure h does not have this property. Figure 9 shows an example of
an acyclic food web W with two vertices u and v for which v is a direct prey
of u but hW(v) > hW(u).
Figure 9. An acyclic food web.
In this example, hW(u) = 6 and hW(v) = 8.
The problem we have found can be avoided if we modify our definition of
level of one species relative to another: If v is a direct or indirect prey of u,
then the level of v relative to u is the length of the longest directed path from u
to v.
It is not hard to show that if h is defined by the same formula as before, but
using the new definition of level, then h satisfies Axioms 1–3 as well as having
the property that any species has higher status than any of its direct or indirect
prey (see reference [5]). The problem we encountered here demonstrates one of
the difficulties with the axiomatic approach. Our problem lay in the definition
of level and this would not show up in any consideration of the reasonableness of
the axioms. Ideally, all of the terms used in specifying the axioms should either
be left undefined or else be checked for “reasonableness”, just as the axioms
themselves are. In this light we would also have to examine the new definition
of level.
Without referring to the notion of relative level in a food web, perhaps the
only requirement we can state for a measure of trophic status is that if there is
a directed path from u to v, then tW(u) tW(v).
There are other ways to investigate complexity of food webs and relative
importance of species in food webs. General methods of measuring complexity
in graphs can be applied to competition graphs and food webs. For example,
such ideas as the number of edges divided by the number of vertices, and the
average out-degree and average in-degree might be useful. The importance, or
criticality, of a species in a food web could be studied by investigating what
happens to the web when the species is deleted from the web. For example, if
the web is disconnected when a species is removed that would indicated a high
level of importance. More information on these questions can be found in [6].

Explanation / Answer

Case study:

Trophic status is a useful means of classifying lakes and describing lake processes in terms of the productivity of the system. Basins with infertile soils release relatively little nitrogen and phosphorus leading to less productive lakes, classified as oligotrophic or mesotrophic.

The quantities of nitrogen, phosphorus, and other biologically useful nutrients are the primary determinants of a body of water's trophic state index (TSI). Nutrients such as nitrogen and phosphorus tend to be limiting resources in standing water bodies, so increased concentrations tend to result in increased plant growth, followed by corollary increases in subsequent trophic levels .Consequently, a body of water's trophic index may sometimes be used to make a rough estimate of its biological condition..Although the term "trophic index" is commonly applied to lakes, any surface water body may be indexed.

Trophic classifications

Lakes are commonly classified according to their trophic state, a term that describes how “green” the lake is as measured by the amount of algae biomass in the water. Three trophic state categories are used to describe lakes as they grow progressively greener: oligotrophic, mesotrophic, and eutrophic. Watershed managers typically do not determine trophic state by directly measuring algae biomass, however. Instead, they indirectly assess it by doing the following: (1) Measuring the levels of nutrients and chlorophyll a in the lake (the primary photosynthetic pigment found in plant cells) (2) Measuring lake water clarity using a Secchi disk

Relationships between Trophic Index (TI), chlorophyll (Chl), phosphorus (P, both micrograms per litre), Secchi depth (SD, metres), and Trophic Class

TI

Chl

P

SD

Trophic Class

<30—40

0—2.6

0—12

>8—4

Oligotrophic

40—50

2.6—20

12—24

4—2

Mesotrophic

50—70

20—56

24—96

2—0.5

Eutrophic

70—100+

56—155+

96—384+

0.5—<0.25

Hypereutrophic

Oligotrophic lakes generally host very little or no aquatic vegetation and are relatively clear, while eutrophic lakes tend to host large quantities of organisms, including algal blooms. Each trophic class supports different types of fish and other organisms, as well. If the algal biomass in a lake or other water body reaches too high a concentration (say >80 TI), massive fish die-offs may occur as decomposing biomass deoxygenates the water.

Example current Scenario: Determining Existing Trophic State:

Lake Mesotroph is a pristine Mid-Atlantic Piedmont lake. The lake surface area is 10 square miles, and has an average depth of 20 feet. Its 250 square mile watershed is entirely forested. The county government and the state jointly own much of the land in this watershed. In order to stimulate the local economy, these governments are considering a sale of the property, to be developed as two-acre lots over approximately 90 square miles of the watershed (watershed imperviousness of about 5%). These homes would be seasonal, primarily operating during half of the year, and served by septic systems. A study is being conducted to estimate the impacts of this potential development, and in particular whether the change would shift the lake trophic status. Existing Conditions Monitoring in the area suggests that the forested land use exports only 0.1 lbs/acre of phosphorus per year, and that total streamflow represents approximately 15 watershed inches of runoff per year. The flushing rate, p, of the lake is determined as: p = (15 in/year)(250 mi2)/[(12 in/ft)(10 mi2 )(20 ft)] = 1.55/yr and the flushing rate times lake mean depth, pZ, is determined as: pZ = 1.55/yr x 20 ft = 31ft/yr With the current land use, and including atmospheric deposition, the total annual load to the lake is 19,200 lbs/year. With the lake area of 10 square miles (6,400 acres), the current lake areal load is 3 lbs/acre/year. Using Vollenweider’s model as illustrated in Figure 2, we can see that the lake is mesotrophic.

Future requirements: Projected Future Conditions The future land use plan will convert 90 square miles of the 250 square mile Lake Mesotroph watershed to two-acre lots (i.e., 11% imperviousness for the land use, 5% imperviousness for the watershed), and assuming seasonal septic operation, it is calculated that the external load to the lake will increase to 38,400 lbs/year (up from 19,200 lbs/year), or an areal loading rate of 6 lbs/acre/ year.

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