Analytic Steady-state Space Use Patterns and Rapid Computations in Mechanistic Home Range Analysis
DSpace/Manakin Repository
Analytic Steady-state Space Use Patterns and Rapid Computations in Mechanistic Home Range Analysis
| Title: | Analytic Steady-state Space Use Patterns and Rapid Computations in Mechanistic Home Range Analysis |
| Author: |
Barnett, Alex H.; Moorcroft, Paul
Note: Order does not necessarily reflect citation order of authors. |
| Citation: | Barnett, Alex H., and Paul R. Moorcroft. 2008. Analytic steady-state space use patterns and rapid computations in mechanistic home range analysis. Journal of Mathematical Biology 57(1): 139-159. |
| Access Status: | Full text of the requested work is not available in DASH at this time (“dark deposit”). For more information on dark deposits, see our FAQ. |
| Full Text & Related Files: |
Moorcroft_AnalyticSteady-state.pdf (614.3Kb; PDF) 
|
| Abstract: | Mechanistic home range models are important tools in modeling animal dynamics in spatially complex environments. We introduce a class of stochastic models for animal movement in a habitat of varying preference. Such models interpolate between spatially implicit resource selection analysis (RSA) and advection-diffusion models, possessing these two models as limiting cases. We find a closed-form solution for the steady-state (equilibrium) probability distribution u* using a factorization of the redistribution operator into symmetric and diagonal parts. How space use is controlled by the habitat preference function w depends on the characteristic width of the animals' redistribution kernel: when the redistribution kernel is wide relative to variation in w, u*. w, whereas when it is narrow relative to variation in w, u* alpha w(2). In addition, we analyze the behavior at discontinuities in w which occur at habitat type boundaries, and simulate the dynamics of space use given two-dimensional prey-availability data, exploring the effect of the redistribution kernel width. Our factorization allows such numerical simulations to be done extremely fast; we expect this to aid the computationally intensive task of model parameter fitting and inverse modeling. |
| Published Version: | http://dx.doi.org/10.1007/s00285-007-0149-8 |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:3128715 |
| Downloads of this work: |
This item appears in the following Collection(s)
-
FAS Scholarly Articles [15469]
Contact administrator regarding this item (to report mistakes or request changes)
