R: List of Movement Models

Movement models {openCR}

R Documentation

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 `\infty`)	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 13, 2106–2118. 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.