A hierarchical path-segmentation movement ecology framework

Abstract

This paper lays out a hierarchical, appropriate-complexity framework for conceptualizing movement-path segments at different spatiotemporal scales in a way that facilitates comparative analyses and bridges behavior and mathematical concepts. It then outlines a process for generating a multimode, multiscale stochastic simulation model that can be used to test animal movement hypotheses and make predictions of movement responses to management and global change. Many methods for analyzing movement data begin by generating step-length (SL) and turning-angle (TA) distributions from relocation time-series data, some of which are linked to ecological, landscape, and environmental covariates. The frequency at which these data are collected may vary from sub-seconds to several hours. The kinds of questions that may be asked of these data, however, are very much scale dependent. The hierarchical path-segmentation (HPS) framework presented here clarifies how the scale at which SL and TA data are collected relates to other sub- and super-diel scales. Difficulties arise because the information contained in SL and TA time series are often not directly relatable to the physiological, ecological, and sociological factors that drive the structure of movement paths at longer scales. These difficulties are overcome by anchoring the classification of movement types around the concept of fixed-period (24 h) diel activity routines and providing a bridge between behavioral/ecological and stochastic-walk concepts (means, variances, correlations, individual-state and local environmental covariates). This bridge is achieved through the generation of relatively short segments conceived as characteristic sequences of fundamental movement elements. These short segments are then used to characterize longer canonical-activity-mode segments that emerge through movement at behaviorally relevant sub-diel scales. HPS thus provides a novel system for integrating sub-minute movement sequences into canonical activity modes (CAMs) that, in turn, can be strung together into various types of diel activity routines (DARs). These DARs both vary among individuals within a given day, and for any given individual across time and under the influence of landscape factors. An understanding of how DARs are influenced by environmental inputs will help us predict the response of supra-diel lifetime movement phases (LiMPs) of individuals, as well as their complete lifetime tracks (LiTs), to anthropogenically induced global change.

Categorizing the geometry of animal diel movement patterns with examples from high-resolution barn owl tracking

“…1 ) for the purpose of DAR categorization: net displacement, bee-line distance between start and end points of each DAR; maximum displacement, distance from the starting point to the most distant point in the DAR data; maximum diameter, the greatest distance between any two points on the trajectory, with the line segment joining these two points referred to as the diameter; maximum width, the sum of the maximum distances of the trajectory points from either side of the diameter line. We chose these metrics because they are much less sensitive to variation in sampling rates than distance-traveled-along-the-track or turning angles [ 6 ], and they capture the spatial geometry of the DAR as a whole, including extent (or scale), elongation, and openess.…”

“…These data are then used to address questions of why and how animals move and when and where they go [ 4 , 5 ]. To address these questions in the era of big data [ 3 ], a framework is required for classifying the movement tracks of animals, as well as segments of such tracks at various spatiotemporal scales [ 6 , 7 ].…”

“…Although some segments of animal movement tracks may be usefully classified using a random or stochastic walk typology [ 8 – 10 ], ultimately many segments need to reflect both underlying landscape structures and the motivation of individuals in the context of their emergent behavioral ecology. It has been proposed that the lifetime track (LiT) of any individual can undergo a hierarchical segmentation process ([ 6 ], also see Glossary) that parses LiTs into segments of various lengths, based on two pivotal concepts: fundamental movement elements (FuMEs) and diel activity routines (DARs). FuMEs are elemental biomechanical movements and constitute a set of building blocks out of which all movement track segments are constructed (much as DNA is constructed from pairs of nucleic acids).…”

“…In this paper, we propose a method for categorizing DARs in a way that should prove useful to building models of how individuals may respond to changes in their environment (climate and landscape). This method takes cognizance of the central and anchoring role that DARs play in a hierarchical deconstruction of the LiT of an individual [ 6 ]. If these DAR segments can be related to environmental factors, then a reassembling of DAR segments under different environmental factors provides a way to anticipate how the structure of LiTs may respond to environmental change.…”

“…At each hierarchical level, we need to categorize the different types of segments that may occur—their length, amount of space covered, and various modes of movement. At the canonical activity mode (CAM) level (e.g., corresponding to behavioral modes of searching, resting, or heading towards desired locations) [ 15 – 18 ], several or many of which constitute one DAR [ 6 ], various local path metrics (i.e., computed using a sliding window along the relocation data time series) are likely useful for discriminating among CAM types. Examples of such metrics include tortuosity, distance moved, and area searched.…”