Deriving movement properties and the effect of the environment from the Brownian bridge movement model in monkeys and birds

Abstract

BackgroundThe Brownian bridge movement model (BBMM) provides a biologically sound approximation of the movement path of an animal based on discrete location data, and is a powerful method to quantify utilization distributions. Computing the utilization distribution based on the BBMM while calculating movement parameters directly from the location data, may result in inconsistent and misleading results. We show how the BBMM can be extended to also calculate derived movement parameters. Furthermore we demonstrate how to integrate environmental context into a BBMM-based analysis.ResultsWe develop a computational framework to analyze animal movement based on the BBMM. In particular, we demonstrate how a derived movement parameter (relative speed) and its spatial distribution can be calculated in the BBMM. We show how to integrate our framework with the conceptual framework of the movement ecology paradigm in two related but acutely different ways, focusing on the influence that the environment has on animal movement. First, we demonstrate an a posteriori approach, in which the spatial distribution of average relative movement speed as obtained from a “contextually naïve” model is related to the local vegetation structure within the monthly ranging area of a group of wild vervet monkeys. Without a model like the BBMM it would not be possible to estimate such a spatial distribution of a parameter in a sound way. Second, we introduce an a priori approach in which atmospheric information is used to calculate a crucial parameter of the BBMM to investigate flight properties of migrating bee-eaters. This analysis shows significant differences in the characteristics of flight modes, which would have not been detected without using the BBMM.ConclusionsOur algorithm is the first of its kind to allow BBMM-based computation of movement parameters beyond the utilization distribution, and we present two case studies that demonstrate two fundamentally different ways in which our algorithm can be applied to estimate the spatial distribution of average relative movement speed, while interpreting it in a biologically meaningful manner, across a wide range of environmental scenarios and ecological contexts. Therefore movement parameters derived from the BBMM can provide a powerful method for movement ecology research.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0043-8) contains supplementary material, which is available to authorized users.

Detecting the influence of environmental covariates on animal movement: a semivariance approach

“…; Buchin et al . ) provide tools for mapping movement attributes (such as velocity) in space, which can then be compared to environmental covariates. The approach presented here is less mechanistic, but provides a direct way for testing for effects of environmental covariates on movement patterns.…”

“…We consider the here proposed framework as an additional tool complementing other existing techniques. For example, recent extensions to Brownian Bridges Movement Models (Horne et al 2007;Buchin et al 2015) provide tools for mapping movement attributes (such as velocity) in space, which can then be compared to environmental covariates. The approach presented here is less mechanistic, but provides a direct way for testing for effects of environmental covariates on movement patterns.…”

Implications of tag positioning and performance on the analysis of cetacean movement

“…We selected 20 locations from the standard error ellipse (based on the CRW output) around each location, creating 20 sets of locations for each track. Around each of these sets, we estimated the probability of occurrence across a regularized raster (1069 × 825 m resolution) using Brownian bridges, following a similar procedure as [21,27]. The resulting 20 distributions were then combined into one average UD by taking the mean value for each raster cell.…”

Model-Based Segmentation and Classification of Trajectories

“…This observation is supported by experimental evaluation. We segmented trajectories from several data sets from movement ecology, collected from fishers [15,16], gulls [20] and vervet monkeys [6], with input sizes up to n = 111, 192 and m = 5, 000. The largest number of changes in the value of last in any single column of a table in these experiments is w = 12, so much smaller than m. In our experiments, w did not depend strongly on n and m: There were many trajectories of varying lengths in the data that had w close to 12 and w usually did not change when increasing m. The results of our experiments are summarised in Table 1.…”