From Fine-Scale Foraging to Home Ranges: A Semivariance Approach to Identifying Movement Modes across Spatiotemporal Scales

Abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. abstract: Understanding animal movement is a key challenge in ecology and conservation biology. Relocation data often represent a complex mixture of different movement behaviors, and reliably decomposing this mix into its component parts is an unresolved problem in movement ecology. Traditional approaches, such as composite random walk models, require that the timescales characterizing the movement are all similar to the usually arbitrary data-sampling rate. Movement behaviors such as long-distance searching and fine-scale foraging, however, are often intermixed but operate on vastly different spatial and temporal scales. An approach that integrates the full sweep of movement behaviors across scales is currently lacking.Here we show how the semivariance function (SVF) of a stochastic movement process can both identify multiple movement modes and solve the sampling rate problem. We express a broad range of continuous-space, continuous-time stochastic movement models in terms of their SVFs, connect them to relocation data via variogram regression, and compare them using standard model selection techniques. We illustrate our approach using Mongolian gazelle relocation data and show that gazelle movement is characterized by ballistic foraging movements on a 6-h timescale, fast diffusive searching with a 10-week timescale, and asymptotic diffusion over longer timescales.

When the going gets tough: behavioural type-dependent space use in the sleepy lizard changes as the season dries

“…Future analyses can expand our binary scale comparison to a broader range of scales (e.g. [56]), and explore whether BTs differ in the spatial scales they respond to.…”

Maximum-entropy description of animal movement

“…Brownian motion is a special case of both OU motion, in the limit of an infinite home-range area, and IOU motion, in the limit of impersistent movement. Recently, animal tracking data sets that are sampled finely enough to estimate velocity and long enough to estimate home-range behavior have spurred the development of an OUF model that generalizes OU and IOU motion [11,12]. Here we provide a theoretical framework that explains this coincident grouping of movement models in terms of continuity and entropy and predicts a missing model within the same group that corresponds to central-place foraging.…”

“…The class of maximum-entropy states we derive is found to include within it Brownian motion (BM) [1][2][3], OrnsteinUhlenbeck (OU) motion [4][5][6][7], integrated OU motion [8][9][10], and a more general movement model that includes all of the previous models as limiting cases [11]. In contrast with Brownian motion, which diffuses endlessly, the OU process is bound to a finite domain.…”

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

“…Blackwell et al (2015) overcome these limitations by modelling location and allowing for a rich class of behavioural processes dependent on both environmental covariates and time via continuous-time Markov chains. A set of models able to incorporate a range of movement assumptions including the home range movement of Blackwell et al (2015) are given in Fleming et al (2014), basing inference on the semivariance function of the underlying movement. This approach offers a flexible range of models, but the user is unable to associate behaviours directly with environmental information or identify the behavioural state of the animal at a specific point in time.…”