movementmodels: List of Movement Models

In openCR: Open Population Capture-Recapture

List of Movement Models

Description

Movement of activity centres between primary sessions is modelled in openCR as a random walk with step length governed by a circular probability kernel. The argument ‘movementmodel’ defines the kernel in several functions. More detail is provided in the vignettes openCR-vignette.pdf.

Movement models in openCR 2.2

Kernel models:

Kernel		Description		Parameters
BVN		bivariate normal		move.a
BVE		bivariate Laplace		move.a
BVC		bivariate Cauchy distribution		move.a
BVT		bivariate t-distribution (2Dt of Clark et al. 1999)		move.a, move.b
RDE		exponential distribution of distance moved cf Ergon and Gardner (2014)		move.a
RDG		gamma distribution of distance moved cf Ergon and Gardner (2014)		move.a, move,b
RDL		log-normal distribution of distance moved cf Ergon and Gardner (2014)		move.a, move.b
RDLS*		log-sech distribution of distance moved (Van Houtan et al. 2007)		move.a, move.b
UNI		uniform within kernel radius, zero outside		(none)
BVNzi		zero-inflated BVN		move.a, move.b
BVEzi		zero-inflated BVE		move.a, move.b
RDEzi		zero-inflated RDE		move.a, move.b
UNIzi		zero-inflated UNI		move.a

* incomplete implementation

Kernel-free models (buffer dependent):

Model		Description		Parameters
IND		independent relocation within habitat mask (Gardner et al. 2018)		(none)
INDzi		zero-inflated IND		move.a

Relationships among models

Some models may be derived as special cases of others, for example

General		Condition		Equivalent to
BVT		large move.b (df ∞)		BVN
BVT		move.b = 0.5 (df 1)		BVC
RDG		move.b = 1		RDE
RDG		move.b = 2		BVE
BVNzi		large move.a		UNIzi

RDL and RDG are almost indistinguishable when move.b > 2.

Deprecated names of movement models

These old names appeared in earlier releases. They still work, but may be removed in future.

Old		New
normal		BVN
exponential		BVE
t2D		BVT
frE		RDE
frG		RDG
frL		RDL
uniform		UNI
frEzi		RDEzi
uniformzi		UNIzi

Additional movement models that may be removed without notice

Kernel		Description		Parameters
annular		non-zero only at centre and edge cells (after clipping at kernelradius)		move.a
annularR		non-zero only at centre and a ring of cells at radius R		move.a, move.b

“annularR” uses a variable radius (R = move.b x kernelradius x spacing) and weights each cell according to the length of arc it intersects; “annularR” is not currently allowed in openCR.fit. For the ‘annular’ models 'move.a' is the proportion at the centre (probability of not moving).

References

Clark, J. S, Silman, M., Kern, R., Macklin, E. and HilleRisLambers, J. (1999) Seed dispersal near and far: patterns across temperate and tropical forests. Ecology 80, 1475–1494.

Efford, M. G. and Schofield, M. R. (2022) A review of movement models in open population capture–recapture. Methods in Ecology and Evolution. In press. https://doi.org/10.1111/2041-210X.13947

Ergon, T. and Gardner, B. (2014) Separating mortality and emigration: modelling space use, dispersal and survival with robust-design spatial capture–recapture data. Methods in Ecology and Evolution 5, 1327–1336.

Gardner, B., Sollmann, R., Kumar, N. S., Jathanna, D. and Karanth, K. U. (2018) State space and movement specification in open population spatial capture–recapture models. Ecology and Evolution 8, 10336–10344 doi: 10.1002/ece3.4509.

Nathan, R., Klein, E., Robledo-Arnuncio, J. J. and Revilla, E. (2012) Dispersal kernels: review. In: J. Clobert et al. (eds) Dispersal Ecology and Evolution. Oxford University Press. Pp. 187–210.

Van Houtan, K. S., Pimm, S. L., Halley, J. M., Bierregaard, R. O. Jr and Lovejoy, T. E. (2007) Dispersal of Amazonian birds in continuous and fragmented forest. Ecology Letters 10, 219–229.

See Also

make.kernel, gkernel, dkernel, pkernel, qkernel

openCR documentation built on Sept. 25, 2022, 5:06 p.m.