Turchin, P., 1997. Quantitative Analysis of Movement, Sinauer Associates, Sunderland, MA. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Stochastic Differential Equations: A Tool for.. - Preisler.. (Correct)
....equations. For example, the deterministic partial differential equation ) 2 1 t x u x t x u x t = 1) has been used to characterize the steady state probability density, u(x, t) of beetles [2] coyote [3] and other free ranging animal population [4] [5]. A univariate stochastic differential equation (SDE) is defined by ) t dB t Y dt t Y t dY = 2) where Y(t) is a random variable, B(t) t 0 is a random process, and is a set of parameters, some known and some unknown. The parameter (Y,t, dY(t) Y(s) s t dt is ....
Turchin, P.: Quantitative Analysis of Movement. Sunderland, Massachusetts (1998)
The Use Of Potential Functions In Modelling Animal Movement - Brillinger, Preisler.. (2001) (Correct)
....of second order partial derivatives taken in the two possible orders, separately for daytime and nighttime data. The final section reviews some of the merits and limitations of employing the potential function to model animal movement. References describing models for animal movement include: [6, 9, 10, 18, 27]. Reference [18] sets down deterministic differential equations (DDEs) for density functions describing the expected pattern of space use by coyotes being influenced by the accumulation and decay of scent marks, also described by DDEs. This is to be contrasted with the approach in [4, 20] where ....
Turchin, P. (1998). Quantitative Analysis of Movement. Sinauer, Sunderland,
Canonical Functions for Dispersal-Induced Synchrony - Bjørnstad, Bolker (2000) (Correct)
....remain exponential or Bessel regardless of the strength of regulation (gure 3) Note that the Bessel function tails o slightly faster than the exponential, but is otherwise closely related. In fact, this function can be approximated as r 1=2 e cr , where c is some constant; see, for example, Turchin 1998. The dispersal distance distribution features centrally in our attempts to understand spatial synchrony. Turchin (1998) reviews how individual movement translates into various theoretical redistribution kernels. We will not try to repeat this discussion here. It is, however, useful to discuss ....
....o slightly faster than the exponential, but is otherwise closely related. In fact, this function can be approximated as r 1=2 e cr , where c is some constant; see, for example, Turchin 1998. The dispersal distance distribution features centrally in our attempts to understand spatial synchrony. Turchin (1998) reviews how individual movement translates into various theoretical redistribution kernels. We will not try to repeat this discussion here. It is, however, useful to discuss three prototypical models pertaining to dispersal kernels resulting from simple rules of movement (in the absence of ....
[Article contains additional citation context not shown here]
Turchin, P. 1998 Quantitative analysis of movement . Sunderland, MA: Sinauer Associates.
Simulating flocks on the wing: The fuzzy approach - Bajec, Zimic, Mraz (2005) (Correct)
No context found.
Turchin, P., 1997. Quantitative Analysis of Movement, Sinauer Associates, Sunderland, MA.
The Effect Of Dispersal Patterns On Stream Populations - Lutscher, Pachepsky, Lewis (2004) (Correct)
No context found.
P. Turchin. Quantitative Analysis of Movement. Sinauer Assoc., Sunderland, MS, 1998.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC