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Abstract Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioural states to help understand why animals move across the landscape. While there are a variety of methods that make behavioural inferences from biotelemetry data, some features of these methods (e.g. analysis of a single data stream, use of parametric distributions) may limit their generality to reliably discriminate among behavioural states.To address some of the limitations of existing behavioural state estimation models, we introduce a nonparametric Bayesian framework called the mixed‐membership method for movement (M4), which is available within the open‐sourcebayesmoveR package. This framework can analyse multiple data streams (e.g. step length, turning angle, acceleration) without relying on parametric distributions, which may capture complex behaviours more successfully than current methods. We tested our Bayesian framework using simulated trajectories and compared model performance against two segmentation methods (behavioural change point analysis (BCPA) and segclust2d), one machine learning method [expectation‐maximization binary clustering (EMbC)] and one type of state‐space model [hidden Markov model (HMM)]. We also illustrated this Bayesian framework using movements of juvenile snail kitesRostrhamus sociabilisin Florida, USA.The Bayesian framework estimated breakpoints more accurately than the other segmentation methods for tracks of different lengths. Likewise, the Bayesian framework provided more accurate estimates of behaviour than the other state estimation methods when simulations were generated from less frequently considered distributions (e.g. truncated normal, beta, uniform). Three behavioural states were estimated from snail kite movements, which were labelled as ‘encamped’, ‘area‐restricted search’ and ‘transit’. Changes in these behaviours over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations.Our nonparametric Bayesian framework estimated behavioural states with comparable or superior accuracy compared to the other methods when step lengths and turning angles of simulations were generated from less frequently considered distributions. Since the most appropriate parametric distributions may not be obvious a priori, methods (such as M4) that are agnostic to the underlying distributions can provide powerful alternatives to address questions in movement ecology. 

Abstract Maintaining the ability of organisms to move between suitable patches of habitat despite ongoing habitat loss is essential to conserving biodiversity. Quantifying connectivity has therefore become a central focus of conservation planning. A large number of metrics have been developed to estimate potential connectivity based on habitat configuration, matrix structure and information on organismal movement, and it is often assumed that metrics explain actual connectivity. Yet, validation of metrics is rare, particularly across entire landscapes undergoing habitat loss—a crucial problem that connectivity conservation aims to mitigate.We leveraged a landscape‐scale habitat loss and fragmentation experiment to assess the performance of commonly used patch‐ and landscape‐scale connectivity metrics against observed movement data, test whether incorporating information about the matrix improves connectivity metrics and examine the performance of metrics across a gradient of habitat loss. We tested whether 38 connectivity metrics predict movement at the patch (i.e. patch immigration rates) and landscape (i.e., total movements) scale for a pest insect, the cactus bugChelinidea vittiger, across 15 replicate landscapes.Metrics varied widely in their ability to explain actual connectivity. At the patch scale, dPCflux, which describes the contribution of a patch to movement across the landscape independent of patch size, best explained immigration rates. At the landscape scale, total movements were best explained by a mesoscale metric that captures that distance between clusters of patches (i.e. modules). Incorporating the matrix did not necessarily improve the ability of metrics to predict actual connectivity. Across the habitat loss gradient, dPCfluxwas sensitive to habitat amount.Synthesis and applications. Our study provides a much‐needed evaluation of network connectivity metrics at the patch and landscape scales, emphasizing that accurate quantification of connectivity requires the incorporation, not only of habitat amount but also habitat configuration and information on dispersal capability of species. We suggest that variation in habitat may often be more critical for interpreting network connectivity than the matrix, and advise that connectivity metrics may be sensitive to habitat loss and should therefore be applied with caution to highly fragmented landscapes. Finally, we recommend that applications integrate mesoscale configuration of habitat into connectivity strategies.