Background. Movement is central to understanding the ecology of animals. The most easily definable segments of an individual’s lifetime track (i.e., movement path defined by a relocation data time series) are its diel activity routines (DARs). This definability is due to fixed start and end points set by a 24-hour clock that depends on the individual’s quotidian schedule. An analysis of day-today variation in the DARs of individuals and the questions that can be asked, particularly in the context of lunar and annual cycles, and in comparison with other individuals, depends the relocation frequency of movement data. Here we present methods for categorizing the spatio-temporal structure of DARs when the data sampling frequency is on the order of minutes or higher. Methods. Our method involves an initial categorization of DARs with regard to the data pooled across all individuals. We approached this categorization using a Ward clustering algorithm that employs four metrics of trajectory shape (though other measures may equally well be used): 1. openness (distance between start and endpoints, aka net displacement), 2. maximum displacement from start point, 3. maximum diameter, and 4. maximum width. We illustrate the general approach using reverse-GPS data obtained from 44 barn owls, Tyto alba, in northeastern Israel. Results. We clustered 6,230 individual DARs into 7 categories. The first component of principle components analysis, which had the interpretation of DAR size, explained 86.5% of variation. The second, which essentially measure DAR openness, explained 8.4% of variation. We also constructed spatio-temporal distributions of DAR types for individuals and groups of individuals aggregated by age, sex, and seasonal trimester, as well as analyzed the idiosyncratic behavior of individuals within family groups in relation to landscape features. Conclusion. Insights into the types and distributions 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. anisms of organismal movement, analyzed primarily using relocation data, often collected

Background There is growing attention to individuality in movement, its causes and consequences. Similarly to other well-established personality traits (e.g., boldness or sociability), conspecifics also differ repeatedly in their spatial behaviors, forming behavioral types (“spatial-BTs”). These spatial-BTs are typically described as the difference in the mean-level among individuals, and the intra-individual variation (IIV, i.e., predictability) is only rarely considered. Furthermore, the factors determining predictability or its ecological consequences for broader space-use patterns are largely unknown, in part because predictability was mostly tested in captivity (e.g., with repeated boldness assays). Here we test if (i) individuals differ in their movement and specifically in their predictability. We then investigate (ii) the consequences of this variation for home-range size and survival estimates, and (iii) the factors that affect individual predictability. Methods We tracked 92 barn owls (Tyto alba) with an ATLAS system and monitored their survival. From these high-resolution (every few seconds) and extensive trajectories (115.2 ± 112.1 nights; X̅ ± SD) we calculated movement and space-use indices (e.g., max-displacement and home-range size, respectively). We then used double-hierarchical and generalized linear mix-models to assess spatial-BTs, individual predictability in nightly max-displacement, and its consistency across time. Finally, we explored if predictability levels were associated with home-range size and survival, as well as the seasonal, geographical, and demographic factors affecting it (e.g., age, sex, and owls’ density). Results Our dataset (with 74 individuals after filtering) revealed clear patterns of individualism in owls’ movement. Individuals differed consistently both in their mean movement (e.g., max-displacement) and their IIV around it (i.e., predictability). More predictable individuals had smaller home-ranges and lower survival rates, on top and beyond the expected effects of their spatial-BT (max-displacement), sex, age and ecological environments. Juveniles were less predictable than adults, but the sexes did not differ in their predictability. Conclusion These results demonstrate that individual predictability may act as an overlooked axis of spatial-BT with potential implications for relevant ecological processes at the population level and individual fitness. Considering how individuals differ in their IIV of movement beyond the mean-effect can facilitate understanding the intraspecific diversity, predicting their responses to changing ecological conditions and their population management.

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.

Background. There is growing attention to individuality in movement, its causes and consequences. Accumulating evidence demonstrates that conspecifics differ repeatably in their spatial behaviors, forming behavioral types (“spatial-BTs”), similarly to other well-established personality traits (e.g., boldness or sociability). Spatial-BTs are typically described as the difference in the mean level among individuals, while the intra-individual variation (IIV, i.e., predictability) is only rarely considered. Studies identifying the factors that determine predictability or its ecological consequences for broader space use patterns are even more sparse, in part because it was mostly tested in captivity (e.g., with repeated boldness assays). Here we test if [i] individuals differ in their movement and specifically in their predictability. We then investigate [ii] the consequences of this variation for home range size and survival estimates, and [iii] the factors that affect individual predictability. Methods. We tracked 92 barn owls (Tyto alba) at very high resolution (every few seconds) using an ATLAS system, and monitored their survival. We calculated movement and space-use indices (e.g., max-displacement and home-range size, respectively) from the trajectories (115.2 ± 112.1 nights; X̅±SD) and then used double-hierarchical and general mix models to assess spatial-BTs, individual predictability in nightly max-displacement, and its consistency across time. Finally, we explored if predictability levels were associated with home-range estimates and survival, as well as the seasonal and demographic factors (age, sex) that affect it. Results. Our dataset revealed clear patterns of individualism in owls’ movement. Individuals differed consistently both in their mean movement (e.g., max-displacement) and their IIV around it (i.e., predictability). Further, more predictable individuals had smaller home-ranges and lower survival rates, on top and beyond the expected effects of their spatial-BT (max-displacement), sex, and age. Juveniles were less predictable than adults, but sexes did not differ in their predictability. Conclusion. These results demonstrate that individual predictability may act as an overlooked axis of spatial-BT with potential implications for relevant ecological processes at the population level and individual fitness. Considering how individuals differ in their IIV of movement beyond the mean-effect can facilitate understanding the intraspecific diversity, predicting their responses to changing ecological conditions and their population management.