Dynamic Models of Animal Movement with Spatial Point Process Interactions

James C. Russell, Ephraim M. Hanks, Murali Haran

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

When analyzing animal movement, it is important to account for interactions between individuals. However, statistical models for incorporating interaction behavior in movement models are limited. We propose an approach that models dependent movement by augmenting a dynamic marginal movement model with a spatial point process interaction function within a weighted distribution framework. The approach is flexible, as marginal movement behavior and interaction behavior can be modeled independently. Inference for model parameters is complicated by intractable normalizing constants. We develop a double Metropolis–Hastings algorithm to perform Bayesian inference. We illustrate our approach through the analysis of movement tracks of guppies (Poecilia reticulata).

Original languageEnglish (US)
Pages (from-to)22-40
Number of pages19
JournalJournal of Agricultural, Biological, and Environmental Statistics
Volume21
Issue number1
DOIs
StatePublished - Mar 1 2016

Fingerprint

Spatial Point Process
dynamic models
Poecilia
Dynamic models
Animals
Dynamic Model
Animal Models
Poecilia reticulata
animal
Interaction
animals
Statistical Models
statistical models
Weighted Distributions
Normalizing Constant
Metropolis-Hastings Algorithm
Bayesian inference
Model
Statistical Model
Movement

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Agricultural and Biological Sciences (miscellaneous)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

@article{907004eeacf44d2a9a03564ba4a769f5,
title = "Dynamic Models of Animal Movement with Spatial Point Process Interactions",
abstract = "When analyzing animal movement, it is important to account for interactions between individuals. However, statistical models for incorporating interaction behavior in movement models are limited. We propose an approach that models dependent movement by augmenting a dynamic marginal movement model with a spatial point process interaction function within a weighted distribution framework. The approach is flexible, as marginal movement behavior and interaction behavior can be modeled independently. Inference for model parameters is complicated by intractable normalizing constants. We develop a double Metropolis–Hastings algorithm to perform Bayesian inference. We illustrate our approach through the analysis of movement tracks of guppies (Poecilia reticulata).",
author = "Russell, {James C.} and Hanks, {Ephraim M.} and Murali Haran",
year = "2016",
month = "3",
day = "1",
doi = "10.1007/s13253-015-0219-0",
language = "English (US)",
volume = "21",
pages = "22--40",
journal = "Journal of Agricultural, Biological, and Environmental Statistics",
issn = "1085-7117",
publisher = "Springer New York",
number = "1",

}

Dynamic Models of Animal Movement with Spatial Point Process Interactions. / Russell, James C.; Hanks, Ephraim M.; Haran, Murali.

In: Journal of Agricultural, Biological, and Environmental Statistics, Vol. 21, No. 1, 01.03.2016, p. 22-40.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Dynamic Models of Animal Movement with Spatial Point Process Interactions

AU - Russell, James C.

AU - Hanks, Ephraim M.

AU - Haran, Murali

PY - 2016/3/1

Y1 - 2016/3/1

N2 - When analyzing animal movement, it is important to account for interactions between individuals. However, statistical models for incorporating interaction behavior in movement models are limited. We propose an approach that models dependent movement by augmenting a dynamic marginal movement model with a spatial point process interaction function within a weighted distribution framework. The approach is flexible, as marginal movement behavior and interaction behavior can be modeled independently. Inference for model parameters is complicated by intractable normalizing constants. We develop a double Metropolis–Hastings algorithm to perform Bayesian inference. We illustrate our approach through the analysis of movement tracks of guppies (Poecilia reticulata).

AB - When analyzing animal movement, it is important to account for interactions between individuals. However, statistical models for incorporating interaction behavior in movement models are limited. We propose an approach that models dependent movement by augmenting a dynamic marginal movement model with a spatial point process interaction function within a weighted distribution framework. The approach is flexible, as marginal movement behavior and interaction behavior can be modeled independently. Inference for model parameters is complicated by intractable normalizing constants. We develop a double Metropolis–Hastings algorithm to perform Bayesian inference. We illustrate our approach through the analysis of movement tracks of guppies (Poecilia reticulata).

UR - http://www.scopus.com/inward/record.url?scp=84958044848&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958044848&partnerID=8YFLogxK

U2 - 10.1007/s13253-015-0219-0

DO - 10.1007/s13253-015-0219-0

M3 - Article

AN - SCOPUS:84958044848

VL - 21

SP - 22

EP - 40

JO - Journal of Agricultural, Biological, and Environmental Statistics

JF - Journal of Agricultural, Biological, and Environmental Statistics

SN - 1085-7117

IS - 1

ER -