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.
REFERENCES
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.
According to field observations in sites close to those where the dispersal kernel was measured, the fecundity of this species is of the order of , and its postdispersal seed-to-adult survival probability is (see Ref. 7 and Refs. 25 and 26 therein). Thus we estimate the net reproductive rate, , to be in the range of . The age at first reproduction (generation time) of the same species is (Ref. 18, Table 1).
Several days of computing time are not enough for the bimodal kernel; in contrast, for the kernel the necessary computing time is about (these results have been obtained for a virtual grid of nodes and using a personal computer with an Intel Pentium III, RAM and ).
For histogram data, in Ref. 34 it has been argued that for some specific kernels and parameter values. However, negative-displacement jumps 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 , provided of course that jumps with are taken into account in one dimension. For nonhistogram data, the authors of Ref. 34 report that 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 ].