Population Dynamics with Nonlinear Diffusion
David Perry, Jessica Schaefer, Brian Schilling, Matthew Williams
Abstract
We consider reaction diffusion models in population dynamics where the per capita
growth rate is a logistic type or a weak Allee type. In particular, we study the effects
of nonlinear diffusion (arising due to aggregative population movements) on the steady
states. We obtain our results via the quadrature method.
1
Introduction
In this paper we discuss steady state reaction diffusion equations with Dirichlet boundary
conditions arising in population dynamics. These equations are of the form:
(
˜
−(dφ(u)u′)′ = uf(u),
x ∈ (0, 1)
(1.1)
u(0) = 0 = u(1).
˜
Here u is the population density, f(u)
is the per capita growth rate, dφ(u) represents the
diffusion coefficient where d > 0 is a constant and φ(u) > 0 for u ≥ 0.
˜ > 0, f˜ decreasing
In particular, we consider per capita growth rates of logistic type (f(0)
˜
and becomes negative for large u) and Allee effect type (f increasing for small values of u
but eventually decreases and becomes negative for large u) and we examine the combined
effects of f˜(u) and φ(u) on the positive steady state solutions.
We note that one can rewrite (1.1) (see Section 5.1) as
(
˜ −1 (v)), x ∈ (0, 1)
−v ′′ = λψ −1 (v)f(ψ
(1.2)
v(0) = 0 = v(1)
Ru
where λ = d1 , ψ(u) := 0 φ(s)ds, and v = ψ(u). We include the following examples in our
study (see [3], [4], [8], [9] and [10]) for the biological relevance of many of these examples):
˜ = a − bu, a, b > 0,
f(u)
˜ = (u + a)(b − u), b > a > 0,
f(u)
˜ = (m − u) − ( K ); m > K > 0, K ≤ 1
f(u)
1+u
˜ = (m − u) − ( K ); m > K > 0, K > 1
f(u)
1+u
ψ(u) = u
ψ(u) = eαu − 1
ψ(u) = u3 − Bu2 + Cu,
where B, C > 0, B 2 − 3C < 0
(when B 2 − 3C < 0, φ(u) = ψ ′ (u) > 0).
1
(Logistic)
(Weak Allee Effect)
(Logistic with Predation)
(Weak Allee with Predation)
(Linear Diffusion)
(Nonlinear Diffusion)
(Nonlinear Diffusion)
The study of positive solutions to (1.1) when ψ(u) = u has significant history (see [3] and
the references within), however, much less is known about the nonlinear case (arising due
to aggregative population movements). In this paper we pursue this study by analyzing the
combined effects of ψ(u) and f˜(u) on the positive steady states. We do this by studying (1.2)
via the quadrature method which is described briefly in Section 2. For further information
on the quadrature method see [1], [2] and [6]. In Section 3, we study (1.2) when ψ(u) = u
˜
and f(u)
is logistic. In Section 4, we discuss the case when ψ(u) = u and f˜(u) displays
Allee effect with f˜(0) > 0 (weak Allee effect). In Section 5, we examine the case when ψ(u)
is nonlinear and its effect on the two per capita growth rate cases; logistic and weak Allee
effect. Finally in Section 6, we discuss population models with constant yield harvesting,
comparing examples with linear and nonlinear diffusion.
2
Quadrature Method
In this section we will analyze the positive solutions to the following equation:
(
−u′′ (x) = λf (u(x)), x ∈ (0, 1)
u(0) = 0 = u(1),
(2.1)
where f : [0, ∞) → (0, ∞) is a C 1 function and λ is a nonnegative parameter. By the
Picard’s theorem, it follows that a positive solution must be symmetric about x = 21 which
increases on (0, 21 ) and decreases on ( 12 , 1). We establish the existence of solutions to (2.1)
by the following theorem:
Theorem 2.1. Let u be a positive solution to (2.1) with u( 12 ) = ρ > 0. Such a solution
exists iff
√
√ Z ρ
dt
p
λ
(2.2)
G(ρ) := 2
=
F (ρ) − F (t)
0
Ru
where F (u) = 0 f (s)ds.
The solution u(x) is described by
Z u(x)
√
dt
p
= 2λx; x ∈ [0, 12 ].
(2.3)
F (ρ) − F (t)
0
Proof. Let u be a positive solution to (2.1) with the property that u′(x0 ) = 0 with x0 ∈ (0, 1).
Observe that both v(x) := u(x0 + x) and w(x) := u(x0 − x) satisfy the following initial value
problem:
−z ′′ (x) = λf (z(x))
z(0) = u(x0 )
z ′ (0) = 0
for x ∈ [0, c) where c = min{x0 , 1−x0 }. This implies that u(x0 +x) = u(x0 −x) ∀x ∈ [0, c).
Since u is a positive solution to (2.1), it must be symmetric around x = 21 , at which point it
has a maximum u( 12 ) = ρ.
2
Multiplying (2.1) by u′ (x),
[u′ (x)]2
−
2
Rs
where F (s) = 0 f (z)dz.
Integrating both sides,
′
u′ (x)
p
Integrating again,
Z
=
F (ρ) − F (u(x))
u(x)
dt
p
0
Since u( 12 ) = ρ, we have,
ρ
0
√
=
F (ρ) − F (t)
√ Z
G(ρ) := 2
= λ[F (u(x))]′
p
2λ;
√
x ∈ [0, 12 ).
2λx;
x ∈ [0, 21 ].
√
dt
= λ.
F (ρ) − F (t)
(2.4)
(2.5)
(2.6)
(2.7)
Therefore, if there exists a solution u such that u( 12 ) = ρ, then ρ must be such that it
√
satisfies the equation G(ρ) = λ. If we have such a ρ, we can define u by
Z
0
u(x)
p
√
dt
= 2λx;
F (ρ) − F (t)
x ∈ [0, 21 ].
By the Implicit Function Theorem, u is differentiable and hence
u′ (x) =
Differentiating again we obtain
p
2λ[F (ρ) − F (u(x))].
−u′′ (x) = λf (u(x)).
Thus u is a positive solution to (2.1) with
u( 21 ) = ρ if f
√
λ = G(ρ).
Remark 2.2. Note that in the above discussion, f (ρ) > 0 and F (ρ) > F (s) ∀s ∈ [0, ρ).
Hence G(ρ) is well defined. In order to use the quadrature method in more general cases we
must choose a ρ within the set S in which f (ρ) is positive and F (ρ) > F (z), ∀z ∈ [0, ρ).
Lemma 2.3. G(ρ) is continuous and differentiable on the set S with
√ Z 1 H(ρ) − H(ρs)
′
G (ρ) := 2
3 ds
0 [F (ρ) − F (ρs)] 2
3
(2.8)
where
u
H(u) = F (u) − f (u)
2
(2.9)
(See [1]).
3
Logistic Type Growth
In this section we consider the boundary value problem
˜
−u′′ = λuf(u)
u(0) = 0 = u(1)
(3.1)
˜
˜
where f(u)
is a C 1 decreasing function such that f˜(r) = 0 for some r > 0, and f(0)
is a
positive constant.
Ž
fHuL
u
r
Figure 1: f˜(u) vs. u
Using the quadrature method, we establish the following precise bifurcation diagram:
Ρ
r
Λ
Π2
€€€€€€€€
Ž €€€€€€
f H0L
Figure 2: ρ vs. λ
To do so we establish the following theorems:
Theorem 3.1. limρ→r− [G(ρ)]2 = ∞.
4
˜ = a − bu, where a and b are positive constants.
when f(u)
Remark 3.2. We note that r =
a
b
Theorem 3.3. limρ→0 [G(ρ)]2 =
π2
.
f˜(0)
Theorem 3.4. G′ (ρ) > 0, ∀ ρ ∈ S = (0, r).
Remark 3.5. It follows that for logistic type reactions, in the case of time dependent prob2
lems, for any positive initial data, the solution eventually dies out if λ < f˜π(0) , and approaches
the unique positive steady state if λ >
3.1
π2
f˜(0)
(unconditional persistence).
Proofs of Theorems 3.1, 3.3, and 3.4
Now we prove Theorems 3.1, 3.3, and 3.4. Let f (u) = uf˜(u).
Proof of Theorem 3.1. By the Mean Value Theorem, we have,
F (ρ) − F (s) = F ′ (c)(ρ − s)
for some c ∈ [s, ρ].
Clearly there exists N > 0 such that f (z) ≤ N(r − z) ∀z ∈ (0, r). Hence,
F (ρ) − F (s) = F ′ (c)(ρ − s)
= f (c)(ρ − s)
≤ N(r − c)(ρ − s)
= N(r − s)2
Therefore,
with n =
1
p
√1 .
N
Since
√ Z
n 2
F (ρ) − F (s)
ρ
0
≥
n
r−s
√
ds
= −n 2 ln(r − s)|ρ0
r−s
approaches infinity as ρ → r , G(ρ) also approaches infinity. Thus, [G(ρ)]2 → ∞ as ρ → r − .
−
Proof of Theorem 3.3.
√ Z
lim G(ρ) = lim 2
ρ→0
ρ→0
ρ
0
√ Z
= lim 2
ρ→0
1
0
√ Z
= lim 2
ρ→0
0
5
1
ds
p
p
q
F (ρ) − F (s)
ρds
F (ρ) − F (ρs)
ds
F (ρ)−F (ρs)
ρ2
By the Lebesgue Dominated Convergence Theorem, the limit can be used inside the
integral and
F (ρ) − F (ρs)
f (ρ) − sf (ρs)
= lim
2
ρ→0
ρ→0
ρ
2ρ
′
f (ρ) − s2 f ′ (ρs)
= lim
ρ→0
2
f ′ (0)
=
(1 − s2 ).
2
lim
Hence
limρ→0 G(ρ) = p
and limρ→0 [G(ρ)]2 =
π2
.
f˜(0)
2
f ′ (0)
Z
0
1
√
ds
π
π
=p
=q
,
2
′
1−s
f (0)
˜
f(0)
Proof of Theorem 3.4. Recall H(z) = F (z) − z2 f (z) (see(2.9)). Then
1
H ′(z) = [f (z) − zf ′ (z)]
2
1
= (z f˜(z) − z[z f˜′ (z) + f˜(z)])
2
1
= − z 2 f˜′ (z) > 0
2
for z > 0, since f˜′ (z) < 0 for logistic type per capita growth rates. Therefore H(z) > 0
and increasing for z > 0.
HHzL
z
Figure 3: H(z) vs. z
Thus, H(ρ) − H(ρs) > 0 for every ρ > 0 and s ∈ [0, 1), and G′ (ρ) > 0 for every ρ ∈ (0, r)
(by using 2.8).
6
3.2
Logistic Type Growth Examples
In this section we present bifurcation diagrams for logistic type examples.
Here we consider a logistic type example f˜(u) = 10 − u.
10
Ρ
10
8
8
6
6
4
4
2
2
4
2
6
8
5
Π2
€€€€€€€
10
10
15
10
20
Λ
Figure 4: f˜(u) vs. u for f˜(u) = 10 − u and the corresponding bifurcation diagram.
Next we consider a logistic type example f˜(u) = 8 − u3 − u4 .
Ž
fHuL
Ρ
8
1.4
1.2
7
1
6
0.8
0.6
5
0.4
0.2
4
0.2
0.4
0.6
0.8
1
1.2
1.4
4
Π2 2
€€€€€€€
8
u
6
8
10
Λ
Figure 5: f˜(u) vs. u for f˜(u) = 8 − u3 − u4 and the corresponding bifurcation diagram.
The following is a predation example with f˜(u) = (10 − u) −
9.5
0.5
.
1+u
Ρ
10
9.4
8
9.3
6
9.2
4
9.1
2
0.1
0.2
0.3
0.4
0.5
0.6
5
0.7
˜
Figure 6: f(u)
vs. u for f˜(u) = (10 − u) −
0.5
1+u
7
10
15
20
25
30
35
Λ
and the corresponding bifurcation diagram.
4
Weak Allee Effect Type Growth
In this section we consider the boundary value problem
˜
−u′′ = λuf(u)
u(0) = 0 = u(1)
(4.1)
where f˜(u) is a C 1 function increasing on [0, α) and decreasing on (α, r), f˜(0) is positive,
˜ = 0 for some r > 0.
and f(r)
f˜(u)
α
r
u
Figure 7: f˜(u) vs u
Using the quadrature method, we establish that the bifurcation diagrams look like:
ρ
r
π2
m
λ∗
π2
˜
f (0)
λ
Figure 8: ρ vs λ
Namely we establish the following theorems:
Theorem 4.1. limρ→r− [G(ρ)]2 = ∞.
Theorem 4.2. If λ <
π2
,
m
then (4.1) has no positive solutions. (Here m = f˜(α))
8
˜
Remark 4.3. We note that r = b when f(u)
= (u + a)(b − u), where a and b are positive
constants with b > a.
π2
.
f˜(0)
Theorem 4.4. limρ→0 [G(ρ)]2 =
Theorem 4.5. G′ (ρ) < 0, for ρ small.
Theorem 4.6. G′ (ρ) > 0, for ρ near r.
Remark 4.7. It follows that for weak Allee effect type reactions, in the case of time dependent
problems, for any positive initial data, the solution eventually dies out if λ < λ∗ (see Figure
2
2
8) and approaches the unique positive steady state if λ > f˜π(0) , while if λ ∈ (λ∗ , f˜π(0) ) the
steady state of the solutions depends on the size of the initial data (conditional persistence).
4.1
Proofs of Theorems 4.1, 4.2, 4.4, 4.5, and 4.6
Proof of Theorem 4.1. Proof follows similar arguments as in Theorem 3.1
Proof of Theorem 4.2. Suppose u is a positive solution. Then
−u′′ = λf (u) ≤ λum
where m = f˜(α). Hence
Z
1
′′
(−u φ)dx ≤
0
where φ = sin(πx). Integrating, we get
Z
1
′′
1
(λumφ)dx
0
1
Z
(u′ φ′ )dx
0
Z 1
=−
(uφ′′ )dx
Z 10
=
(π 2 uφ)dx.
(−u φ)dx =
0
Z
0
Therefore
This is possible only if λ ≥
π2
.
m
Z
1
2
0
(π uφ)dx ≤
Z
1
(λmuφ)dx.
0
Hence for
λ<
π2
m
(4.1) has no positive solutions.
Proof of Theorem 4.4. Proof follows similar arguments as in Theorem 3.3
9
Proof of Theorems 4.5 and 4.6. Recall Equation (2.8)
√ Z 1 H(ρ) − H(ρs)
′
G (ρ) = 2
3 .
0 (F (ρ) − F (ρs)) 2
2
Now H(s) = F (s) − 2s f (s) and H ′(s) = − s2 f˜′ (s). Thus, H decreases on (0, α) and increases
on (α, r) with lims→r H(s) = F (r) > 0. That is H has the shape:
HHsL
FHrL
s
Α
Σ
r
Figure 9: H(s) vs s
Hence, clearly for ρ < α, H(ρ) − H(ρs) < 0 for all s ∈ [0, 1) and for ρ > σ, H(ρ) − H(ρs) > 0
for all s ∈ [0, 1]. This implies that G′ (ρ) < 0 for ρ < α and G′ (ρ) > 0 for ρ > σ.
4.2
Weak Allee Effect Type Growth Examples
In this section we present bifurcation diagrams for weak Allee effect type examples. Here we
˜ = (u + 3)(10 − u).
consider a weak Allee effect type example f(u)
Ž
fHuL
Ρ
10
40
15
€€€€€€€
2
20
5
2
4
6
8
10
12
u
5
€€€€
2
-20
1
€€€€
6
-40
Π2
€€€€€€€
30
1
€€€€
2
Λ
˜
˜ = (u + 3)(10 − u) and the corresponding bifurcation diagram
Figure 10: f(u)
vs. u for f(u)
10
u
Next we consider a weak Allee effect type example f˜(u) = 10sin( 10
) + 10.
Ž
fHuL
Ρ
15Π
20
40
15
30
10
20
10
5
10
20
30
40
0.5
u
Λ
Π2
€€€€€€€
10
1.5
2
2.5
3
u
Figure 11: f˜(u) vs. u for f˜(u) = 10 sin( 10
) + 10 and the corresponding bifurcation diagram
The following is a predation example with f˜(u) = (10 − u) − 7 .
1+u
Ρ
5
8
4
6
3
2
4
1
2
2
4
6
8
10
2
˜
˜ = (10 − u) −
Figure 12: f(u)
vs. u for f(u)
5
7
1+u
3
4
5
6
7
Λ
and the corresponding bifurcation diagram.
Nonlinear Diffusion
In this section we analyze the case of nonlinear diffusion with the types of per capita growth
rates discussed in Sections 3 and 4. We consider Equation (1.1)
˜
−(dφ(u)u′)′ = uf(u),
x ∈ (0, 1)
u(0) = 0 = u(1)
where φ(u) is a non constant function of u. We show that nonlinear diffusion can produce
conditional persistence even when f˜ is of the logistic type and produce unconditional persistence even when f˜ is of the weak Allee type. Examples of such behavior are included. For
ease of analysis, we study the equivalent form of (1.1), namely
˜
−[ψ(u)]′′ = λuf(u),
x ∈ (0, 1)
(5.1)
u(0) = 0 = u(1)
where
ψ(u) =
Z
u
φ(z) dz.
(5.2)
0
Note that (5.1) is obtained by differentiating (5.2) and substituting
d
(ψ(u)) = φ(u)u′
dx
into (1.1) and letting d = λ1 .
Throughout in Section 5 we assume both f˜ and ψ are C 3 functions.
11
(5.3)
5.1
Analysis of the Nonlinear Diffusion Equation
In this section we define a function L(u) which will later be used to determine whether the
combined effect of ψ(u) and f˜(u) is of logistic or weak Allee type.
Note that (5.2) clearly shows that ψ(0) = 0. Since ψ ′ (u) = φ(u) > 0 for all u ≥ 0 we can
define the one-to-one function
v = ψ(u).
(5.4)
Since v is one-to-one, ψ −1 exists and so solving for v is equivalent to solving for u. Thus, we
can rewrite (5.1) as
˜ −1 (v)), x ∈ (0, 1)
−v ′′ = λψ −1 (v)f(ψ
(5.5)
v(0) = 0 = v(1).
Letting
g̃(v) =
˜ −1 (v))
ψ −1 (v)f(ψ
v
(5.6)
we finally obtain
−v ′′ = λvg̃(v).
(5.7)
Therefore, we can analyze solutions to (5.1) in precisely the same manner as we analyzed
solutions to (1.1) when the diffusion was linear by studying g̃(v). Now, since v = ψ(u),
g̃(v) =
˜
uf(u)
= L(u) (say).
ψ(u)
(5.8)
Since u, g̃(v), and ψ(u) are all differentiable, L(u) is also differentiable (for u > 0). However,
L(u) is clearly not defined at u = 0 whereas (5.1) is. We extend L(u) to be defined at u = 0
by letting
L(0) = lim L(u).
(5.9)
u→0
Applying L’Hopitals’ rule to (5.8) and evaluating at u = 0 we obtain
lim L(u) =
u→0
and so
L(u) =
(
f˜(0)
ψ ′ (0)
uf˜(u)
ψ(u)
f˜(0)
ψ′ (0)
u>0
u=0
(5.10)
(5.11)
which is the function we study. Note that
g̃ ′(v) = L′ (u)
and hence g̃ ′(v) and L′ (u) have the same sign.
12
du
dv
(5.12)
5.2
Logistic Type Reactions
In this section we introduce a class of ψ(u) whose diffusive effects will produce a combined
˜
Allee effect type growth even with f(u)
of logistic type.
˜ + 2f˜′ (0)ψ ′ (0) > 0 then solutions
Theorem 5.1. For a given logistic type f˜(u), if −ψ ′′ (0)f(0)
to (5.1) will exhibit a combined Allee effect.
Proof. Note that L(u) =
uf˜(u)
.
ψ(u)
L′ (u) =
Thus
′
˜
˜
ψ(u)[uf˜′(u) + f(u)]
− uf(u)ψ
(u)
.
2
(ψ(u))
(5.13)
By continuity of L′ (u), we know that
L′ (0) = lim L′ (u).
(5.14)
u→0
In order to find this limit, L’Hopital’s rule is used twice on (5.13) and we obtain
L′ (0) =
−ψ ′′ (0)f˜(0) + 2f˜′(0)ψ ′ (0)
.
2(ψ ′ (0))2
(5.15)
Hence if we apply our hypothesis to (5.15) we obtain
L′ (0) > 0
(5.16)
which implies a combined Allee effect.
Corollary 5.1. It is necessary that ψ ′′ (0) < 0 in order to produce a combined Allee effect
from logistic type f˜(u).
Proof. Suppose that ψ ′′ (0) ≥ 0. Then since f˜(0) > 0, ψ ′ (0) > 0 and f˜′ (0) < 0 we have
that
−ψ ′′ (0)f˜(0) + 2f˜′ (0)ψ ′ (0) < 0
(5.17)
which implies that L′ (0) < 0, and so a combined Allee effect will not occur.
5.2.1
Examples
Consider the logistic growth f˜(u) = a − bu and ψ(u) = u3 − Bu2 + Cu with B > 0, C > 0,
B 2 < 3C, and Ba > Cb. Then
and
Hence
ψ(0) = 0,
ψ ′ (u) = 3u2 − 2Bu + C > 0
ψ ′′ (u) = 6u − 2B < 0
(since
B 2 < 3C),
if
u < B3 .
˜ + 2f˜′ (0)ψ ′ (0) = 2Ba − 2Cb > 0.
−ψ ′′ (0)f(0)
(5.18)
(5.19)
Thus L′ (0) > 0 and a combined Allee effect is produced. Letting a = 10, b = 1, B = 1, and
C = 5 we produced the following figures.
13
10
0.2
8
0.4
0.6
1
0.8
1.95
6
4
1.9
2
1.85
4
2
6
8
1.8
10
f˜(u) vs u
L(u) vs u
˜
Figure 13: f(u)
and L(u)
Ρ
Ρ
10
10
8
8
6
6
4
4
2
2
1
2
3
4
5
6
7
Λ
5.25
5.5
5.75
6
6.25
6.5
Λ
ψ(u) = u3 − u2 + 5u
f˜(u) = 10 − u
ψ(u) = u
˜
f (u) = 10 − u
Figure 14: Bifurcations Diagrams with linear and nonlinear ψ
Note that f˜′ (u) < 0, ∀ u ≥ 0 whereas L′ (u) > 0 near u = 0. Therefore, in this example, the
diffusive effects of ψ produced an Allee effect from logistic growth. Next, in Figure 17, we
let a = 10, b = 1, B = 1, C = 20 and produce the following figure.
Ρ
10
8
6
4
2
19.8
19.9
20.1
20.2
20.3
Λ
˜ = 10 − u with ψ(u) = u3 − u2 + 20u
Figure 15: Bifurcation diagram for f(u)
Here the parameter B and C violate (5.19). Also no combined Allee effect was produced.
14
Next, we consider a Logistic with predation example of the form
K
f˜(u) = (m − u) − (
); m > K > 0, K ≤ 1.
1+u
In particular, we produce the following bifurcation diagrams with linear and nonlinear diffusion.
Ρ
Ρ
10
10
8
8
6
6
4
4
2
2
5
10
15
20
25
ψ(u) = u
˜
f(u) = (10 − u) −
30
35
Λ
5.25
5.5
5.75
6
6.25
6.5
6.75
Λ
ψ(u) = u3 − u2 + 5u
˜ = (10 − u) − 0.5
f(u)
1+u
0.5
1+u
Figure 16: Bifurcations Diagrams with linear and nonlinear ψ
5.3
Weak Allee Type Reactions
In this section we introduce a class of ψ(u) whose diffusive effects will produce a combined
logistic type growth in otherwise weak Allee type f˜(u). Our analysis is analogous to that of
the previous section.
˜ + 2f˜′(0)ψ ′ (0) ≤ 0 is a necessary
Theorem 5.2. For a weak Allee type f˜(u), −ψ ′′ (0)f(0)
condition for (5.1) to be of logistic type.
Proof. Recall L′ (0) from (5.15), i.e.
L′ (0) =
−ψ ′′ (0)f˜(0) + 2f˜′(0)ψ ′ (0)
.
2(ψ ′ (0))2
(5.20)
Thus if our hypothesis is not satisfied then by (5.20) we obtain
L′ (0) > 0
(5.21)
and the Allee effect will persist.
Corollary 5.2. It is necessary that ψ ′′ (0) > 0 in order to produce a combined logistic type
˜
growth from weak Allee type f(u).
Proof. Suppose that ψ ′′ (0) ≤ 0. Then since f˜(0) > 0, ψ ′ (0) > 0 and f˜′ (0) > 0 we have
that
˜ + 2f˜′ (0)ψ ′ (0) > 0.
−ψ ′′ (0)f(0)
(5.22)
This implies that L′ (0) > 0 and an Allee effect will persist.
15
5.3.1
Examples
Consider f˜(u) = (u + a)(b − u) with b > a > 0 and ψ(u) = eαu − 1 with α >
that
ψ(0) = 0,
ψ ′ (u) = αeαu > 0
and
2(b−a)
.
ab
Note
(5.23)
˜ + 2f˜′ (0)ψ ′ (0) = −α2 (ab) + 2α(b − a) < 0.
−ψ ′′ (0)f(0)
(5.24)
Hence the conditions of Theorem (5.2) are satisfied. If we set α = 2, a = 1, b = 2 then (5.24)
will be satisfied. We produced the following figures:
Ρ
Ρ
0.5
2
0.4
1.5
0.3
1
0.2
0.5
0.1
6
7
8
9
10
11
12
Λ
10.2
10.4
10.6
10.8
11
Λ
ψ(u) = e2u − 1
f˜(u) = (u + 1)(2 − u)
ψ(u) = u
˜
f (u) = (u + 1)(2 − u)
Figure 17: Bifurcation diagrams with linear and nonlinear ψ
It can also be shown that the Allee effect is not only reversed locally near zero, but also
globally. To do so, we show that L′ (u) < 0 for 0 < u < 2 (note that for u > 2, f (u) =
u(u + 1)(2 − u) < 0 and so ρ 6∈ S). The following Mathematica plots of L′ (u) for this
particular example show that L′ (u) < 0 on [0, 2].
0.5
1
1.5
2
-100
-200
-300
-400
-500
Figure 18: L′ (u) for u ∈ [0, 2]
Finally, we consider a weak Allee with predation example of the form
K
f˜(u) = (m − u) − (
); m > K > 0, K > 1.
1+u
In particular, we produce the following bifurcation diagrams with linear and nonlinear diffusion.
16
Ρ
Ρ
5
8
4
6
3
4
2
2
1
2
3
4
5
6
ψ(u) = u
˜
f(u) = (10 − u) −
7
Λ
18
20
22
24
26
28
Λ
ψ(u) = e5u − 1
˜ = (10 − u) − 7
f(u)
1+u
7
1+u
Figure 19: Bifurcations Diagrams with linear and nonlinear ψ
5.4
Strong Allee Effect
˜ < 0), f (u) = uf(u)
˜ is negative
For a strong Allee effect type per capita growth rate (i.e. f(0)
near u = 0 (see figure 13).
Ž
fHuL
Ž
fHuL=ufHuL
u
Q
u
r
˜
f(u)
vs u
f (u) vs. u
˜
Figure 20: General type of strong Allee f and f
This produces conditional persistence for λ large. In fact, the typical bifurcation diagram
for
˜
−u′′ = λuf(u)
(5.25)
u(0) = 0 = u(1)
is as follows:
ρ
r
θ
λ
Figure 21: Typical bifurcation diagram for Strong Allee type f (u)
17
Rz
Here F (θ) = 0 where F (z) = 0 f (t) dt. Now, even with nonlinear diffusion ψ(u) which
is equivalent to studying
˜ −1 (v)) = λg(v)
−v ′′ = λψ −1 (v)f(ψ
(5.26)
v(0) = 0 = v(1),
g(v) still remains negative for v small. Thus a similar bifurcation diagram persists. In
particular, conditional persistence persists for λ large and cannot be altered by the presence
of nonlinear diffusion.
6
Constant Yield Harvesting
In recent literature there has been considerable interest in the effects of constant yield harvesting in population dynamics (see [5], [7] and [8]). In this section we first consider a logistic
type growth model with linear diffusion ψ(u) = u and constant yield harvesting.
(
−u′′ = λ[10u − u2 − c]
(6.1)
u(0) = 0 = u(1).
By using the quadrature method described in Section 2, we obtain the bifurcation diagrams
(ρ vs. λ) as c increases in (0, 18.75) (Note that for Sc to be non-empty, c < 18.75)(see Figure
22). Next we consider the effects produced by the nonlinear diffusion ψ(u) = u3 − u2 + 5u,
by studying the bifurcation diagram for
−(u3 − u2 + 5u)′′ = λ[10u − u2 − c]
(6.2)
u(0) = 0 = u(1)
as c varies, and compare the corresponding bifurcation diagrams (see Figure 23). We note
that with harvesting present, even in the case of logistic type growth, ranges of λ exist where
there is multiplicity of positive steady states. This persists even in the case of these classes
of nonlinear diffusion.
Ρ
10
8
6
4
2
5
10
15
20
25
30
Λ
Figure 22: Bifurcation Diagrams of ρ vs. λ for f (u) = 10u − u2 − c with 0 < c < 18.75
18
Ρ
30
25
20
15
10
5
5
10
15
20
25
30
35
40
Λ
Figure 23: Bifurcation Diagrams of ρ vs. λ for f (u) = 10u − u2 − c with 0 < c < 18.75
and ψ(u) = u3 − u2 + 5u
7
Conclusions
In conclusion, we used a (quadrature) method for analyzing solutions to equation (1.1) and
have shown that solutions will exhibit either logistic or Allee type reactions, depending on
both the growth rate and diffusion. We have established that the combined effects of f˜ and
ψ can produce both Allee and logistic type reactions from both logistic and weak Allee type
f˜. Furthermore, we obtained results on the effects of constant yield harvesting on a model
with logistic growth and nonlinear diffusion.
19
Acknowledgements
This work was performed by the NSF REU students at Mississppi State University during
the Summer of 2005. We would like to extend a special thanks to our mentor Dr. Ratnasingham Shivaji for his guidance throughout this research. We would also like to thank Jaffar
Ali Shahul-Hameed for his assistance and guidance.
Affiliations and Addresses
David Perry - New York University
50 Misty Meadow Lane
New Boston, NH 03070
dwp234@nyu.edu
Jessica Schaefer - Northern Arizona University
PO Box 7501
Flagstaff
AZ 86011
jrs244@dana.ucc.nau.edu
Brian Schilling - Mississippi State University
8609 Melrose Lane
River Ridge
LA 70123
bls153@msstate.edu
Matthew Williams - Clarkson State University
620 Long Pond Road
Plymouth
MA 02360
willi5285@aol.com
williamr@clarkson.edu
20
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