Population Dynamics with Nonlinear Diffusion

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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)u0 )0 = uf˜(u),
u(0) = 0 = u(1).
x ∈ (0, 1)
(1.1)
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.
When φ(u) = u and f˜ is of logistic type (f˜(0) > 0, f˜ decreasing and becomes negative
for large u), there is a rich history of results on the steady states and on the dynamics of the
associated time dependent problem. In particular, for any positive initial data, the solution
2
to the time dependent problem eventually dies out if λ < f˜π(0) , and approaches a unique
2
positive steady state if λ > f˜π(0) (unconditional persistence). Here d = λ1 . However when
φ(u) = u and f˜ is of the Allee effect type (f˜ increasing for small values of u but eventually
decreases and becomes negative for large u), unconditional persistence no longer persists for
all λ. In particular, when f˜(0) > 0 (weak Allee effect), the solution for the time dependent
2
problem eventually dies out if λ < λ∗ (some) with λ∗ < f˜π(0) , and approaches unique positive
π2
,
f˜(0)
2
while if λ ∈ (λ∗ , f˜π(0) ) the steady state of the solution depends
on the size of the initial data (conditional persistence). In the case when f˜(0) < 0 (strong
Allee effect), the solution for the time dependent problem eventually dies out if λ < λ∗∗
(some) while if λ > λ∗∗ , the steady state of the solution depends on the size of the initial
data (conditional persistence). Typical bifurcation diagrams for the logistic and Allee type
problems are given below (see Figure 1).
steady state if λ∗ >
1
u
λ
È
π2
f˜(0)
u
È
λ∗
λ
π2
f˜(0)
u
λ
È
λ∗∗
Figure 1: Bifurcation diagrams: logistic (upper); weak Allee effect (middle);
strong Allee effect (lower).
In this paper, we examine effects of nonlinear diffusion. We study the combined effects of f˜(u) and φ(u) on the positive steady states. In particular, we establish that the
combined effects of f˜(u) and φ can produce both Allee and logistic type reactions (conditional/unconditional persistence) from both logistic and weak Allee types f˜.
We note that one can rewrite (1.1) (see Section 5.1) as
−v 00 = λψ −1 (v)f˜(ψ −1 (v)),
v(0) = 0 = v(1)
2
x ∈ (0, 1)
(1.2)
Ru
where 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):
f˜(u) = a − bu, a, b > 0,
f˜(u) = (u + a)(b − u), b > a > 0,
K
f˜(u) = (m − u) − ( 1+u
); m > K > 0, K ≤ 1
K
˜
f (u) = (m − u) − ( 1+u ); m > K > 0, K > 1
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 0 (u) > 0).
(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:
−u00 (x) = λf (u(x)),
u(0) = 0 = u(1),
x ∈ (0, 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 = 12 which
increases on (0, 12 ) 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
=
G(ρ) := 2
λ
(2.2)
F (ρ) − F (t)
0
Ru
where F (u) = 0 f (s)ds.
3
The solution u(x) is described by
Z
u(x)
0
√
dt
p
= 2λx;
F (ρ) − F (t)
x ∈ [0, 21 ].
(2.3)
Proof. Let u be a positive solution to (2.1) with the property that u0 (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 00 (x) = λf (z(x))
z(0) = u(x0 )
z 0 (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 = 12 , at which point it
has a maximum u( 12 ) = ρ.
Multiplying (2.1) by u0 (x),
µ 0
¶0
[u (x)]2
−
= λ[F (u(x))]0
(2.4)
2
Rs
where F (s) = 0 f (z)dz.
Integrating both sides,
u0 (x)
p
=
F (ρ) − F (u(x))
√
2λ;
x ∈ [0, 12 ).
(2.5)
Integrating again,
Z
u(x)
0
Since u( 12 ) = ρ, we have,
√
dt
p
= 2λx;
F (ρ) − F (t)
√ Z
G(ρ) := 2
ρ
dt
p
0
F (ρ) − F (t)
x ∈ [0, 21 ].
=
√
λ.
(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
u(x)
0
√
dt
p
= 2λx;
F (ρ) − F (t)
x ∈ [0, 21 ].
By the Implicit Function Theorem, u is differentiable and hence
u0 (x) =
p
2λ[F (ρ) − F (u(x))].
Differentiating again we obtain
4
−u00 (x) = λf (u(x)).
Thus u is a positive solution to (2.1) with
u( 12 ) = 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)
0
G (ρ) := 2
3 ds
0 [F (ρ) − F (ρs)] 2
where
u
H(u) = F (u) − f (u)
2
(2.8)
(2.9)
(See [1]).
3
Logistic Type Growth
In this section we consider the boundary value problem
−u00 = 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 2: f˜(u) vs. u
5
Using the quadrature method, we establish the following precise bifurcation diagram:
Ρ
r
Λ
Π2
€€€€€€€€
Ž €€€€€€
f H0L
Figure 3: ρ vs. λ
To do so we establish the following theorems:
Theorem 3.1. limρ→r− [G(ρ)]2 = ∞.
when f˜(u) = a − bu, where a and b are positive constants.
Remark 3.2. We note that r =
a
b
Theorem 3.3. limρ→0 [G(ρ)]2 =
π2
.
f˜(0)
Theorem 3.4. G0 (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 0 (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 0 (c)(ρ − s)
= f (c)(ρ − s)
≤ N (r − c)(ρ − s)
= N (r − s)2
6
Therefore,
with n =
1
n
p
≥
r−s
F (ρ) − F (s)
√1 .
N
Since
√ Z
n 2
ρ
0
√
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
√ Z
= lim 2
ρ→0
√ Z
= lim 2
ρ→0
ρ
p
0
1
0
1
0
ds
F (ρ) − F (s)
ρds
p
F (ρ) − F (ρs)
ds
q
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
0
f (ρ) − s2 f 0 (ρs)
= lim
ρ→0
2
0
f (0)
=
(1 − s2 ).
2
lim
Hence
limρ→0 G(ρ) = p
2
f 0 (0)
and limρ→0 [G(ρ)]2 =
Z
1
√
0
ds
π
π
=p
=q
,
2
0
1−s
f (0)
f˜(0)
π2
.
f˜(0)
Proof of Theorem 3.4. Recall H(z) = F (z) − z2 f (z) (see(2.9)). Then
1
H 0 (z) = [f (z) − zf 0 (z)]
2
1
= (z f˜(z) − z[z f˜0 (z) + f˜(z)])
2
1
= − z 2 f˜0 (z) > 0
2
7
for z > 0, since f˜0 (z) < 0 for logistic type per capita growth rates. Therefore H(z) > 0
and increasing for z > 0.
HHzL
z
Figure 4: H(z) vs. z
Thus, H(ρ) − H(ρs) > 0 for every ρ > 0 and s ∈ [0, 1), and G0 (ρ) > 0 for every ρ ∈ (0, r)
(by using 2.8).
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 5: 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
Π2 2
€€€€€€€
8
u
4
6
8
10
Λ
Figure 6: 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) −
8
0.5
.
1+u
9.5
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 7: f˜(u) vs. u for f˜(u) = (10 − u) −
4
0.5
1+u
10
15
20
25
30
35
Λ
and the corresponding bifurcation diagram.
Weak Allee Effect Type Growth
In this section we consider the boundary value problem
−u00 = 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,
and f˜(r) = 0 for some r > 0.
f˜(u)
α
u
r
Figure 8: f˜(u) vs u
Using the quadrature method, we establish that the bifurcation diagrams look like:
ρ
r
π2
m
λ∗
π2
f˜(0)
Figure 9: ρ vs λ
9
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))
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. G0 (ρ) < 0, for ρ small.
Theorem 4.6. G0 (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
9) 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
−u00 = λf (u) ≤ λum
where m = f˜(α). Hence
Z
Z
1
1
00
(−u φ)dx ≤
(λumφ)dx
0
0
where φ = sin(πx). Integrating, we get
Z 1
Z 1
00
(−u φ)dx =
(u0 φ0 )dx
0
0
Z 1
=−
(uφ00 )dx
0
Z 1
(π 2 uφ)dx.
=
0
Therefore
Z
1
Z
1
2
(π uφ)dx ≤
0
This is possible only if λ ≥
π2
.
m
(λmuφ)dx.
0
Hence for
π2
λ<
m
(4.1) has no positive solutions.
10
Proof of Theorem 4.4. Proof follows similar arguments as in Theorem 3.3
Proof of Theorems 4.5 and 4.6. Recall Equation (2.8)
√ Z 1 H(ρ) − H(ρs)
0
G (ρ) = 2
3 .
0 (F (ρ) − F (ρs)) 2
2
Now H(s) = F (s) − 2s f (s) and H 0 (s) = − s2 f˜0 (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 10: 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 G0 (ρ) < 0 for ρ < α and G0 (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
consider a weak Allee effect type example f˜(u) = (u + 3)(10 − 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
Figure 11: f˜(u) vs. u for f˜(u) = (u + 3)(10 − u) and the corresponding bifurcation diagram
u
Next we consider a weak Allee effect type example f˜(u) = 10sin( 10
) + 10.
11
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
) + 10 and the corresponding bifurcation diagram
Figure 12: f˜(u) vs. u for f˜(u) = 10 sin( 10
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
Figure 13: f˜(u) vs. u for f˜(u) = (10 − 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)u0 )0 = 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)]00 = λuf˜(u), x ∈ (0, 1)
u(0) = 0 = u(1)
where
Z
(5.1)
u
ψ(u) =
φ(z) dz.
(5.2)
0
Note that (5.1) is obtained by differentiating (5.2) and substituting
d
(ψ(u)) = φ(u)u0
dx
into (1.1) and letting d = λ1 .
Throughout in Section 5 we assume both f˜ and ψ are C 3 functions.
12
(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 0 (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
−v 00 = λψ −1 (v)f˜(ψ −1 (v)),
v(0) = 0 = v(1).
Letting
x ∈ (0, 1)
(5.5)
ψ −1 (v)f˜(ψ −1 (v))
g̃(v) =
v
(5.6)
−v 00 = λvg̃(v).
(5.7)
we finally obtain
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 (0)
uf˜(u)
ψ(u)
f˜(0)
ψ 0 (0)
u>0
u=0
(5.10)
(5.11)
which is the function we study. Note that
g̃ 0 (v) = L0 (u)
and hence g̃ 0 (v) and L0 (u) have the same sign.
13
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.
Theorem 5.1. For a given logistic type f˜(u), if −ψ 00 (0)f˜(0) + 2f˜0 (0)ψ 0 (0) > 0 then solutions
to (5.1) will exhibit a combined Allee effect.
Proof. Note that L(u) =
uf˜(u)
.
ψ(u)
Thus
ψ(u)[uf˜0 (u) + f˜(u)] − uf˜(u)ψ 0 (u)
L (u) =
.
(ψ(u))2
0
(5.13)
By continuity of L0 (u), we know that
L0 (0) = lim L0 (u).
(5.14)
u→0
In order to find this limit, L’Hopital’s rule is used twice on (5.13) and we obtain
L0 (0) =
−ψ 00 (0)f˜(0) + 2f˜0 (0)ψ 0 (0)
.
2(ψ 0 (0))2
(5.15)
Hence if we apply our hypothesis to (5.15) we obtain
L0 (0) > 0
(5.16)
which implies a combined Allee effect.
Corollary 5.1. It is necessary that ψ 00 (0) < 0 in order to produce a combined Allee effect
from logistic type f˜(u).
Proof. Suppose that ψ 00 (0) ≥ 0. Then since f˜(0) > 0, ψ 0 (0) > 0 and f˜0 (0) < 0 we have
that
−ψ 00 (0)f˜(0) + 2f˜0 (0)ψ 0 (0) < 0
(5.17)
which implies that L0 (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 0 (u) = 3u2 − 2Bu + C > 0
ψ 00 (u) = 6u − 2B < 0
(since
B 2 < 3C),
if
u < B3 .
−ψ 00 (0)f˜(0) + 2f˜0 (0)ψ 0 (0) = 2Ba − 2Cb > 0.
(5.18)
(5.19)
Thus L0 (0) > 0 and a combined Allee effect is produced. Letting a = 10, b = 1, B = 1, and
C = 5 we produced the following figures.
14
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 14: 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 15: Bifurcations Diagrams with linear and nonlinear ψ
Note that f˜0 (u) < 0, ∀ u ≥ 0 whereas L0 (u) > 0 near u = 0. Therefore, in this example, the
diffusive effects of ψ produced an Allee effect from logistic growth. Next, in Figure 18, 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
Figure 16: Bifurcation diagram for f˜(u) = 10 − u with ψ(u) = u3 − u2 + 20u
Here the parameter B and C violate (5.19). Also no combined Allee effect was produced.
15
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
0.5
f˜(u) = (10 − u) − 1+u
0.5
1+u
Figure 17: 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.
Theorem 5.2. For a weak Allee type f˜(u), −ψ 00 (0)f˜(0) + 2f˜0 (0)ψ 0 (0) ≤ 0 is a necessary
condition for (5.1) to be of logistic type.
Proof. Recall L0 (0) from (5.15), i.e.
L0 (0) =
−ψ 00 (0)f˜(0) + 2f˜0 (0)ψ 0 (0)
.
2(ψ 0 (0))2
(5.20)
Thus if our hypothesis is not satisfied then by (5.20) we obtain
L0 (0) > 0
(5.21)
and the Allee effect will persist.
Corollary 5.2. It is necessary that ψ 00 (0) > 0 in order to produce a combined logistic type
growth from weak Allee type f˜(u).
Proof. Suppose that ψ 00 (0) ≤ 0. Then since f˜(0) > 0, ψ 0 (0) > 0 and f˜0 (0) > 0 we have
that
−ψ 00 (0)f˜(0) + 2f˜0 (0)ψ 0 (0) > 0.
(5.22)
This implies that L0 (0) > 0 and an Allee effect will persist.
16
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 0 (u) = αeαu > 0
and
2(b−a)
.
ab
Note
(5.23)
−ψ 00 (0)f˜(0) + 2f˜0 (0)ψ 0 (0) = −α2 (ab) + 2α(b − a) < 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 18: 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 L0 (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 L0 (u) for this
particular example show that L0 (u) < 0 on [0, 2].
1
0.5
1.5
2
-100
-200
-300
-400
-500
Figure 19: L0 (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.
17
Ρ
Ρ
5
8
4
6
3
4
2
2
1
2
3
4
5
ψ(u) = u
˜
f (u) = (10 − u) −
7
6
Λ
18
20
22
24
26
28
Λ
ψ(u) = e5u − 1
7
f˜(u) = (10 − u) − 1+u
7
1+u
Figure 20: Bifurcations Diagrams with linear and nonlinear ψ
5.4
Strong Allee Effect
For a strong Allee effect type per capita growth rate (i.e. f˜(0) < 0), f (u) = uf˜(u) is negative
near u = 0 (see figure 14).
Ž
fHuL
Ž
fHuL=ufHuL
u
Q
u
r
f˜(u) vs u
f (u) vs. u
Figure 21: General type of strong Allee f˜ and f
This produces conditional persistence for λ large. In fact, the typical bifurcation diagram
for
−u00 = λuf˜(u)
u(0) = 0 = u(1)
(5.25)
is as follows:
ρ
r
θ
λ
Figure 22: Typical bifurcation diagram for Strong Allee type f (u)
18
Here F (θ) = 0 where F (z) =
is equivalent to studying
Rz
0
f (t) dt. Now, even with nonlinear diffusion ψ(u) which
−v 00 = λψ −1 (v)f˜(ψ −1 (v)) = λg(v)
v(0) = 0 = v(1),
(5.26)
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.
−u00 = λ[10u − u2 − c]
u(0) = 0 = u(1).
(6.1)
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
23). Next we consider the effects produced by the nonlinear diffusion ψ(u) = u3 − u2 + 5u,
by studying the bifurcation diagram for
−(u3 − u2 + 5u)00 = λ[10u − u2 − c]
u(0) = 0 = u(1)
(6.2)
as c varies, and compare the corresponding bifurcation diagrams (see Figure 24). 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 23: Bifurcation Diagrams of ρ vs. λ for f (u) = 10u − u2 − c with 0 < c < 18.75
19
Ρ
30
25
20
15
10
5
5
10
15
20
25
30
35
40
Λ
Figure 24: 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.
20
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
[email protected]
Jessica Schaefer - Northern Arizona University
PO Box 7501
Flagstaff
AZ 86011
[email protected]
Brian Schilling - Mississippi State University
8609 Melrose Lane
River Ridge
LA 70123
[email protected]
Matthew Williams - Clarkson State University
620 Long Pond Road
Plymouth
MA 02360
[email protected]
[email protected]
21
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[5] A. Collins, M. Gilliland, C. Henderson, S. Koone, L. McFerrin, E. K. Wampler, Population Models with Diffusion and Constant Yield Harvesting, Rose-Hulman Undergraduate Mathematics Journal Vol 5(2004) Issue 2.
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