Bimodal dispersal probability distributions with characteristic distances differing by several orders of magnitude have been derived and favorably compared to observations by Nathan et al. [Nature (London)418, 409 (2002)]. For such bimodal kernels, we show that two-dimensional molecular dynamics computer simulations are unable to yield accurate front speeds. Analytically, the usual continuous-space random walks (CSRWs) are applied to two dimensions. We also introduce discrete-space random walks and use them to check the CSRW results (because of the inefficiency of the numerical simulations). The physical results reported are shown to predict front speeds high enough to possibly explain Reid’s paradox of rapid tree migration. We also show that, for a time-ordered evolution equation, fronts are always slower in two dimensions than in one dimension and that this difference is important both for unimodal and for bimodal kernels.

1.
A. L.
Garner
,
Y. Y.
Lau
,
T. L.
Jackson
,
M. D.
Uhler
,
D. W.
Jordan
, and
R. M.
Gilgenbach
,
J. Appl. Phys.
98
,
124701
(
2005
).
2.
R. F.
Ismagilov
,
A. D.
Stroock
,
P. J. A.
Kenis
,
G.
Whitesides
, and
H. A.
Stone
,
Appl. Phys. Lett.
76
,
2376
(
2000
).
3.
P. R.
Nair
and
M. A.
Alam
,
Appl. Phys. Lett.
88
,
233120
(
2006
).
5.
D. B.
Olfe
,
AIAA J.
5
,
638
(
1967
).
6.
M. F.
Modest
,
Radiative Heat Transfer
(
McGraw-Hill
,
New York
,
1993
).
7.
R.
Nathan
,
G. G.
Katul
,
H. S.
Horn
,
S. M.
Thomas
,
R.
Oren
,
R.
Avissar
,
S. W.
Pacala
, and
S. A.
Levin
,
Nature (London)
418
,
409
(
2002
).
8.
J.
Fort
and
V.
Méndez
,
Rep. Prog. Phys.
65
,
895
(
2002
).
9.
10.
J.
Fort
and
V.
Méndez
,
Phys. Rev. Lett.
82
,
867
(
1999
).
11.
J.
Fort
and
V.
Méndez
,
Phys. Rev. Lett.
89
,
178101
(
2002
).
12.
K.
Davison
,
P.
Dolukhanov
,
G. R.
Sarson
, and
A.
Shukurov
,
J. Archaeol. Sci.
33
,
641
(
2006
).
13.
M. O.
Vlad
and
J.
Ross
,
Phys. Rev. E
66
,
061908
(
2002
).
14.
J.
Fort
,
D.
Jana
, and
J. M.
Humet
,
Phys. Rev. E
70
,
031913
(
2004
).
15.
J.
Fort
,
J.
Pérez
,
E.
Ubeda
, and
J.
García
,
Phys. Rev. E
73
,
021907
(
2006
).
16.
H. F.
Weinberger
, in
Nonlinear Partial Differential Equations and Applications
, edited by
J.
Chadam
(
Springer
,
Berlin
,
1978
).
17.
H. F.
Wienberger
,
SIAM J. Math. Anal.
13
,
353
(
1982
).
18.
J. S.
Clark
,
Am. Nat.
152
,
204
(
1998
) (especially Fig. 1 and p. 219).
19.
M.
Neubert
and
H.
Caswell
,
Ecology
81
,
1613
(
2000
) (especially Fig. 15).
20.
R.
Nathan
and
G. G.
Katul
,
Proc. Natl. Acad. Sci. U.S.A.
102
,
8251
(
2005
).
21.
M. B.
Soons
,
G. W.
Heil
,
R.
Nathan
, and
G. G.
Katul
,
Ecology
85
,
3056
(
2004
).
22.
V.
Méndez
,
D.
Campos
, and
J.
Fort
,
Europhys. Lett.
66
,
902
(
2004
).
23.
J.
Fort
and
V.
Méndez
,
Phys. Rev. E
60
,
5894
(
1999
).
24.

Note that Eq. (2) does not include saturation. We have checked that including saturation in the 2D simulations, the front speeds are the same. Obviously the same happens in the CSRW and DSRW approaches after linearization.

25.

According to field observations in sites close to those where the dispersal kernel was measured, the fecundity f of this species is of the order of 104seedsdispersedtreeyr, and its postdispersal seed-to-adult survival probability is s0.06% (see Ref. 7 and Refs. 25 and 26 therein). Thus we estimate the net reproductive rate, R0=fs, to be in the range of 660seedstreeyr. The age at first reproduction (generation time) of the same species is T20yr (Ref. 18, Table 1).

26.

The numerical simulations agree with Eq. (2) but not with Eq. (1) with, e.g., R[p(x,y,t)]=R0p(x,y,t).

27.

The minimum speed [Eqs. (3)–(5) for the CSRW; Eq. (11) for the DSRW] is that of the front: this is seen by comparing to simulations (full curve in Fig. 1 for the CSRW; rhombs in Fig. 2 for the DSRW).

28.

The CSRW uses φ(Δ), which is the kernel found in Ref. 7. But the simulations and DSRW use ϕ(Δ). Otherwise the agreement in Figs. 1 and 2 cannot be attained. For the DSRW, see especially Eq. (9).

29.

Several days of computing time are not enough for the bimodal kernel; in contrast, for the kernel φS(Δ) the necessary computing time is about 30min (these results have been obtained for a virtual grid of 10002 nodes and using a personal computer with an Intel Pentium III, 393kbyte RAM and 1133MHz).

30.
J. A. J.
Metz
,
D.
Mollison
, and
F.
Van Den Bosch
,
The Geometry of Ecological Interactions
, edited by
V.
Dieckmann
,
R.
Law
, and
J. A. J.
Metz
(
Cambridge University Press
,
Cambridge
,
2000
).
31.
M.
Kot
,
M. A.
Lewis
, and
P.
van den Driessche
,
Ecology
77
,
2027
(
1996
).
32.
M. R.
Spiegel
,
Mathematical Handbook
(
McGraw-Hill
,
New York
,
1970
).
33.

For histogram data, in Ref. 34 it has been argued that c1D<c2D for some specific kernels and parameter values. However, negative-displacement jumps (Δx<0) in one dimension were apparently omitted [because Eq. (12) instead of (13) in Ref. 34 was apparently used to compute the 1D speeds in Table 1 there]. As the general proof in Sec. VI shows, one will always find that c2D<c1D, provided of course that jumps with Δx<0 are taken into account in one dimension. For nonhistogram data, the authors of Ref. 34 report that c2D<c1D for some specific kernels and parameter values, in agreement with the general proof in Sec. VI in the present paper [but also in this case, their Eq. (9) instead of (8) in Ref. 34 should have been applied, again to take into account jumps with Δx<0].

34.
M. A.
Lewis
,
M. G.
Neubert
,
H.
Caswell
,
J. S.
Clark
, and
K.
Shea
, in
Conceptual Ecology and Invasion Biology: Reciprocal Approaches to Nature
, edited by
M.
Cadotte
,
S.
McMahon
, and
T.
Fukami
(
Kluwer
,
Dordrecht
,
2006
), pp.
169
192
.
You do not currently have access to this content.