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We implemented our method in R. We provide the codes and the bumblebee and black kite datasets in Dryad Digital Repository https://doi.org/10.5061/dryad.47d7wm390 (Pasquaretta et al., 2015). The roe deer dataset was obtained from MOVEBANK (Wikelski & Kays, 2020). Animal Identifier: Sandro (M06), from Cagnacci et al. (2011) (https://www.movebank.org/). The wolf dataset was obtained from MOVEBANK (Wikelski & Kays, 2020), Animal identifier: Zimzik, from Kaczensky et al. (2006) (https://www.movebank.org/).We implemented our method in R. We provide the codes and the bumblebee and black kite datasets in Dryad Digital Repository https://doi.org/10.5061/dryad.47d7wm390 (Pasquaretta et al., 2015). The roe deer dataset was obtained from MOVEBANK (Wikelski & Kays, 2020). Animal Identifier: Sandro (M06), from Cagnacci et al. (2011) (https://www.movebank.org/). The wolf dataset was obtained from MOVEBANK (Wikelski & Kays, 2020), Animal identifier: Zimzik, from Kaczensky et al. (2006) (https://www.movebank.org/).; International audience; 1. Understanding how animal movements change across space and time is a fundamental question in ecology. While classical analyses of trajectories give insightful descriptors of spatial patterns, a satisfying method for assessing the temporal succession of such patterns is lacking.2. Network analyses are increasingly used to capture properties of complex animal trajectories in simple graphical metrics. Here, building on this approach, we introduce a method that incorporates time into movement network analyses based on temporal sequences of network motifs.3. We illustrate our method using four example trajectories (bumblebee, black kite, roe deer, wolf) collected with different technologies (harmonic radar, platform terminal transmitter, global positioning system). First, we transformed each trajectory into a spatial network by defining the animal's coordinates as nodes and movements in between as edges. Second, we extracted temporal sequences of network motifs from each movement network and compared the resulting behavioural profiles to topological features of the original trajectory. Finally, we compared each sequence of motifs with simulated Brownian and Lévy random motions to statistically determine differences between trajectories and classical movement models.4. Our analysis of the temporal sequences of network motifs in individual movement networks revealed successions of spatial patterns corresponding to changes in behavioural modes that can be attributed to specific spatio‐temporal events of each animal trajectory. Future applications of our method to multi‐layered movement and social network analysis yield considerable promises for extending the study of complex movement patterns at the population level.

1. Pollinators, such as bees, face the complex challenge of efficiently exploiting patchily distributed floral resources across large landscapes. 2. In the present study we consider the utility of spatial network statistics to analyse the foraging patterns of bees moving between feeding sites at various spatial and temporal scales. 3. We explain how spatial movement networks can be derived theoretically and experimentally to describe bee foraging decisions. 4. We illustrate this approach by analysing six datasets of bumblebees and honeybees foraging in arrays of artificial flowers, and showing how network metrics change as foragers gain experience with the spatial distribution of feeding sites. 5. We compare network analyses with more conventional statistics used to characterise bee foraging movements and discuss the implications of our novel statistical and modelling approach for pollination ecology.

In many studies, flight initiation distance (FID, the distance at which a prey starts to flee at the approach of a walker) is positively related to starting distance (SD, the distance at which the walker begins to approach) and alert distance (AD, the distance at which the focal individual becomes alert to the threat). In spite of the fundamental differences between SD, a covariate that may not have any biological effect, and AD, a measure related to the behaviour of the animal, it is common to use SD as a proxy for AD when AD is hard to measure (e.g. in species that do not exhibit distinguishable alert postures). However, the relationship between SD and AD or FID may not have any biological reasons, but may instead simply result from a mathematical artefact because of the constraints SD ≥ AD ≥ FID. Under such constrains, the homoscedasticity assumption is violated, and thus, the classical null hypothesis of linear regression (slope = 0) is invalid. In this study, we first show that using SD as a proxy for AD can strongly affect the results on FID. Using data from FID tests on alpine marmots (Marmota marmota), a linear mixed model with AD as a covariate, suggested that the interaction between previous activity and AD had an effect on FID, while this effect was not detected when SD replaced AD as the covariate in the analysis. We then propose that the actual statistical test of the relationship between SD, AD and FID should be based on a null hypothesis that incorporates the constraint SD ≥ AD ≥ FID ≥ 0 and generate 95% CI of simulated slopes obtained from random values under this constraint. This null hypothesis can be rejected if the observed slope of the relationship between two of these variables is outside the 95% CI. We demonstrated that, for alpine marmots, the observed slope of the relationship between AD and SD was within the 95% CI of the simulated slopes. The absence of a statistically significant biological effect in the relationship between SD and AD raises important questions on the outcome of relationship between SD and FID. In Alpine marmot flight, decision should be studied separating the effect of SD on AD and the effect of AD on FID.

Ranging behaviour is one of the most important aspects of the life history of many species. Spatial distributions of individuals in the wild is linked to factors such as foraging, mating, population density, availability of resources and competition. Accurate data on the spatial location of individuals over time is often difficult to collect. Here, we propose a new simple, non-invasive and economic method for collecting accurate spatial data usable for many different species of free-ranging animals. Our instrument for collecting animal locations consists of three elements: a laser range finder, a laser tilt sensor and a protractor. This instrument can obtain three-dimensional parameters of the space from a fixed point allowing the user to collect geographical locations of the animals and, in general, of any point of interest. The device we tested showed a very low average error among (1.76 6 0.643 m) and within (1.79 6 0.058 m) observers, and the locations we obtained were all within the 95% probability of the tolerance intervals for the 20 positions which we measured repeatedly with a Global Position System for each of 10 different test locations. We tested a range of different distances to the target points (from 20 to 222 m), and we propose formulas to calculate precision of the instrument inside this interval. Precision of estimated locations was between 0.32 to 3.55 m from the real location and it was slightly related to distance of the target point (r ¼ 0.38, P ¼ 0.054). As an example of its practical application, we present data on the use of the instrument within the framework of a study on a population of free-ranging individually tagged alpine marmots Marmota marmota.