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

Abstract

Background Movement is central to understanding the ecology of animals. The most robustly definable segments of an individual’s lifetime track are its diel activity routines (DARs). This robustness is due to fixed start and end points set by a 24-h clock that depends on the individual’s quotidian schedule. An analysis of day-to-day variation in the DARs of individuals, their comparisons among individuals, and the questions that can be asked, particularly in the context of lunar and annual cycles, depends on the relocation frequency and spatial accuracy of movement data. Here we present methods for categorizing the geometry of DARs for high frequency (seconds to minutes) movement data. Methods Our method involves an initial categorization of DARs using data pooled across all individuals. We approached this categorization using a Ward clustering algorithm that employs four scalar “whole-path metrics” of trajectory geometry: 1. (distance between start and end points), 2. from start point, 3. , and 4. . We illustrate the general approach using reverse-GPS data obtained from 44 barn owls, Tyto alba, in north-eastern Israel. We conducted a principle components analysis (PCA) to obtain a factor, , that essentially captures the scale of movement. We then used a generalized linear mixed model with as the dependent variable to assess the effects of age and sex on movement. Results We clustered 6230 individual DARs into 7 categories representing different shapes and scale of the owls nightly routines. Five categories based on size and elongation were classified as closed (i.e. returning to the same roost), one as partially open (returning to a nearby roost) and one as fully open (leaving for another region). Our PCA revealed that the DAR scale factor, , accounted for 86.5% of the existing variation. It also showed that captures the openness of the DAR and accounted for another 8.4% of the variation. We also constructed spatio-temporal distributions of DAR types for individuals and groups of individuals aggregated by age, sex, and seasonal quadrimester, as well as identify some idiosyncratic behavior of individuals within family groups in relation to location. Finally, we showed in two ways that DARs were significantly larger in young than adults and in males than females. Conclusion Our study offers a new method for using high-frequency movement data to classify animal diel movement routines. Insights into the types and distributions of the geometric shape and size of DARs in populations may well prove to be more invaluable for predicting the space-use response of individuals and populations to climate and land-use changes than other currently used movement track methods of analysis.

The Statistical Building Blocks of Animal Movement Simulations

“…Thus our simulations show a greater velocity range and a greater absolute turning-angle range, but a smaller relative net-displacement range than our barn owl data. The larger barn owl net-displacement range arises because the owls typically return home each day, but displacement to another resting site occurs on some days [42]. To capture this behavior in our ANIMOVER_1 simulator, we would need to add a movement kernel that is biased to move in the direction of a homing beacon at certain times during the diel cycle.…”

“…The range of values that may be useful to generate in a multi-point construction of this map will depend on the kind of empirical to which this mapping is fitted. Such an exercise is, thus, best left to a detailed study that explores the structure of given set of empirical movement data, such as an extended set of the owl data discussed below—a set containing data collected from at least several tens of individuals (e.g., as in [42, 51]).…”

“…We analyzed relocation data obtained from two barn owls ( Tyto alba ), using an ATLAS reverse GPS technology system that was set up in the Harod valley in northeast Israel [51]. The relocation data for both individuals, an adult female and a juvenile male (GG41259 and GG41269 in the original data set and individuals with IDs 29 and 31 in [42]), were collected a frequency of 0.25 Hz (i.e., one point every 4 seconds) during a several week period in the late summer of 2021. We used a 15-step segmentation, which corresponds to segmenting the movement track into one minute sequences.…”

“…Finally, in this paper, we discuss why the construction of our ANIMOVER_1 RAMP facilitates analyses of movement pathways by ecologists compared with the effort needed to code up movement simulations from scratch using current programming platforms, such as Netlogo [50] or R. In addition, we focus on issues relating to parameter selection and running the model, as well as classifying StaMEs derived from both simulated and real data into a limited number of types (in our case 8 categories using hierarchical clustering methods). The real data relate to the movement data of barn owls ( Tyto alba ) in the Harod Valley, Israel, as more fully discussed in other contexts elsewhere [42, 51, 52].…”

“…A string of same category StaMEs then constitutes a track segment that can be classified as a homogeneous or canonical activity mode (CAM) of a type defined by the underlying category of StaME (e.g., brisk walking might translate into bee-lining and meandering into searching behavior). Characteristic mixtures of CAMs, in turn, can be strung together into identifiable behavioral activity modes (BAMs; e.g., resting, foraging, heading to a known location while being vigilant), with several BAMs coming together each day to form a diel activity routine (DAR) [41, 42] (Fig 1. The DAR itself is a hierarchical segment that can be understood in terms of an invariant 24-hour period for most animals, apart from some deep dwelling marine or cave-dwelling species, because for most species it is a fundamental biological rhythm honed by evolution [43].…”