Species interactions are critical for maintaining community structure and dynamics, but the effects of invasive species on multitrophic networks remain poorly understood. We leveraged an ongoing invasion scenario in Patagonia, Argentina, to explore how non-native ungulates affect multitrophic networks. Ungulates disrupt a hummingbird-mistletoe-marsupial keystone interaction, which alters community composition. We sampled pollination and seed dispersal interactions in intact and invaded sites. We constructed pollination and seed dispersal networks for each site, which we connected via shared plants. We calculated pollination-seed dispersal connectivity, identified clusters of highly connected species, and quantified species' roles in connecting species clusters. To link structural variation to stability, we quantified network tolerance to single random species removal (disturbance propagation) and sequential species removal (robustness) using a stochastic coextinction model. Ungulates reduced the connectivity between pollination and seed dispersal and produced fewer clusters with a skewed size distribution. Moreover, species shifted their structural role, fragmenting the network by reducing the 'bridges' among species clusters. These structural changes altered the dynamics of cascading effects, increasing disturbance propagation and reducing network robustness. Our results highlight invasive species' role in altering community structure and subsequent stability in multitrophic communities.

Hidden Markov models (HMMs) and their extensions have proven to be powerful tools for classification of observations that stem from systems with temporal dependence as they take into account that observations close in time are likely generated from the same state (i.e., class). When information on the classes of the observations is available in advanced, supervised methods can be applied. In this paper, we provide details for the implementation of four models for classification in a supervised learning context: HMMs, hidden semi-Markov models (HSMMs), autoregressive-HMMs, and autoregressive-HSMMs. Using simulations, we study the classification performance under various degrees of model misspecification to characterize when it would be important to extend a basic HMM to an HSMM. As an application of these techniques we use the models to classify accelerometer data from Merino sheep to distinguish between four different behaviors of interest. In particular in the field of movement ecology, collection of fine-scale animal movement data over time to identify behavioral states has become ubiquitous, necessitating models that can account for the dependence structure in the data. We demonstrate that when the aim is to conduct classification, various degrees of model misspecification of the proposed model may not impede good classification performance unless there is high overlap between the state-dependent distributions, that is, unless the observation distributions of the different states are difficult to differentiate. Supplementary materials accompanying this paper appear on-line.

Occupancy models represent a useful tool to estimate species distribution throughout the landscape. Among them, MacKenzie et al.’s model (2002, MC), is frequently used to infer species environmental responses. However, the assumption that detection probability is homogeneous or fully explained by covariates may limit its performance. Species should be more easily observed at sites with a higher number of individuals. We simulated data following occupancy model (RN) that accounts for abundance‐driven heterogeneous detection and two variants with overdispersion in the detection probability and local abundances. Then, we compared the performance of the MC model against that of RN. In addition to model misspecifications, insufficient information in data (i.e. infrequent detections) can limit our ability to detect existing effects with affordable sampling designs. To deal with this source of error, we extended RN approach to a community‐level joint species model (RN‐JSM), where species responses and detectability depended on their traits and phylogeny. Then, we tested RN‐JSM performance in simulated and out‐of‐sample field data. High abundance‐driven heterogeneity in detection (i.e. common and secretive species) limited the ability of the MC model to quantify covariate effects; especially, when the number of visits was low. Both models (MC and RN), often failed to detect existing effects when data were overdispersed. Moreover, the RN model consistently lacked sufficient power when analysing data from uncommon species (even when simulations and model specifications perfectly matched). This problem was solved by our RN‐JSM, which yielded more precise and accurate estimates of species environmental responses. Increased accuracy in rare species held when the RN‐JSM was tested with real and out‐of‐sample datasets. In the light of our results, we propose: (i) for common and secretive species analyse occupancy data with the RN model and prioritize revisiting sites; (ii) for species that may have overdispersed detectability or local abundances (e.g. with correlated behaviours or occurring in clusters), apply RN extensions that account for this extra‐variation (e.g. Poisson‐beta or zero‐inflated models). Finally, (iii) for uncommon species (mean abundances <1), whenever possible, gather data at the community level and apply joint species modelling techniques.

The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.