Flexible hidden Markov models for behaviour-dependent habitat selection

Context Animals’ use of space and habitat selection emerges from their movement patterns, which are, in turn, determined by their behavioural or physiological states and extrinsic factors. Aim The aims of the present study were to investigate animal movement and incorporate the movement patterns into habitat selection analyses using Global Positioning System (GPS) location data from 16 black bears (Ursus americanus) in a fragmented area of Florida, USA. Methods Hidden Markov models (HMMs) were used to discern the movement patterns of the bears. These results were then used in step-selection functions (SSFs) to evaluate habitat selection patterns and the factors influencing these patterns. Key results HMMs revealed that black bear movement patterns are best described by three behavioural states: (1) resting (very short step-lengths and large turning angles); (2) encamped (moderate step-lengths and large turning angles); and (3) exploratory (long step-lengths and small turning angles). Bears selected for forested wetlands and marsh wetlands more than any other land cover type, and generally avoided urban areas in all seasons and when in encamped and exploratory behavioural states. Bears also chose to move to locations farther away from major roads. Conclusions Because habitat selection is influenced by how animals move within landscapes, it is essential to consider animals’ movement patterns when making inferences about habitat selection. The present study achieves this goal by using HMMs to first discern black bear movement patterns and associated parameters, and by using these results in SSFs to investigate habitat selection patterns. Thus, the methodological framework developed in this study effectively incorporates state-specific movement patterns while making inferences regarding habitat selection. The unified methodological approach employed here will contribute to an improved understanding of animal ecology as well as informed management decisions. Implications Conservation plans focused on preserving forested wetlands would benefit bears by not only providing habitat for resting and foraging, but also by providing connectivity through fragmented landscapes. Additionally, the framework could be applied to species that follow annual cycles and may provide a tool for investigating how animals are using dispersal corridors.

Habitat selection of animals is influenced by spatial heterogeneity as well as temporal environmental dynamics. In addition, human activities potentially have severe influences on the habitat selection of animals, often resulting in more nocturnal behavior. We investigated seasonal and circadian habitat selection patterns of red deer (Cervus elaphus) on a military training area in Bavaria (Germany). Individual deer ranged on two neighboring, but non-overlapping sites differing in landscape composition and human activity. Using GPS telemetry data, we visually investigated selection patterns and then fitted step-selection functions to assess multiple approaches to account for temporal (i.e., diel and seasonal) effects and for the impact of habitat conditions on selection. We first showed that the way in which time of day is considered in step-selection functions is essential for obtaining ecologically meaningful results, and that the usual ways of including time (i.e., either based on clock-time or based on night vs. day categories) can lead to incomplete or misleading conclusions. Furthermore, we found that individuals followed either circadian or seasonal habitat selection patterns, depending on the site they inhabited. This can be explained by differences in disturbance between the two sites. Except in winter, individuals selected for open habitats during the night irrespective of human activity level, but only individuals from the more disturbed site selected for covered habitats during the day. Our results highlight the importance of carefully considering the appropriate temporal scale for habitat selection analyses. Our findings also indicate that red deer are not a crepuscular or nocturnal species per se, as is often observed in human-dominated landscapes. Instead, our results imply that nocturnality in red deer is an effect of adaption to human activities.

Habitat selection analysis provides useful information on how animals become distributed in response to spatial heterogeneity. Here, we analyze the habitat use and selection of different water bodies (marsh, swamps and round shallow lakes) by capybaras ( Hydrochoerus hydrochaeris ) and their relation to environmental variables during contrasting climate‐hydrological seasons in the Esteros del Iberá (Corrientes, Argentina). We evaluated the intensity of use by capybaras through the total number of individuals (abundance) in each water body, the number and mean size of social groups, and the physical and vegetation characteristics of the environment. The capybaras used marsh and swamps according to availability in both seasons, while they used rounded shallow lakes less than their available would suggest in summer. The use intensity of different rounded shallow lakes estimated based on group size did not show significant differences. In contrast, significant differences were observed when evaluated by the number of individuals in each rounded shallow lake. Different intensity of use was closely associated with environmental and vegetation characteristics. The results show that habitat suitability for capybaras is associated with vegetation cover and “embalsados” in the low‐lying area rather than with the morphometry of the rounded shallow lakes. The pattern of habitat selection may depend on forage quality, water availability for thermoregulation and mating, and presence of shelter and resting sites. On the other hand, the present study shows how the size, shape and the abundance of different types of water bodies affect population abundance and density.

Understanding animal space us is a fundamental concern in Ecology. Predator-prey interactions are a behavioral response game where predators try to find prey, and prey are trying to avoid the predators, thereby shaping animal space use and habitat selection. In the process both predator and prey tradeoff between risk avoidance and food acquisition in order to maximize their fitness. As such predator-prey dynamics have fascinated many generations of ecologists. At the same time the impact of human activities on ecosystem processes have become increasingly evident over the years and almost all habitats on earth today have been altered to some degree by humans. As a consequence humans can be considered as ecological players of ecosystem processes that shape the behavior and demography of their commensal species. In the presence of humans large top predators may be downgraded in the food chain or compete with humans for their prey. Furthermore prey species themselves have to tradeoff between risk avoidance towards humans or their natural predators. It was the goal of this thesis to look at various aspects involved in shaping the space use patterns of the Eurasian lynx (Lynx lynx) and its main prey, the European roe deer (Capreolus capreolus) and to characterize the spatiotemporal drivers of habitat selection and risk avoidance of both players in a human dominated environment. By focusing on both predators and prey I identify different key components of the behavioral response game between Eurasian lynx and European roe deer. In the first chapter I investigated how tradeoffs between risk avoidance and resource abundance in human-altered environments affect habitat selection of Eurasian lynx. In particular, I studied how spatial scale interacts with the intrinsic behavioral state of Eurasian lynx in shaping the response to temporal and spatial fluctuations in human activity and prey availability. I show that lynx tradeoff between anthropogenic risk and prey density by using areas of high prey density during times of low human disturbance. This tradeoff differs among behavioral states and is dependent on the spatial scale considered. In the second chapter I focused on the prey and tested how lynx and humans interact in shaping space use patterns of roe deer during the hunting season in fall. Multiple predators of a single prey may positively or negatively interact with each other via their prey. The same may be true for human hunters that compete with natural predators for the same prey. I explored how roe deer tradeoff between hunting and lynx predation risk and whether hunting increases the deer’s susceptibility to lynx predation. I found that roe deer avoid areas of high hunting risk during the hunting season at the expense of higher exposure to lynx predation risk. Furthermore there was strong evidence that lynx increased their hunting effort and hunting success during this time period. The results provided strong evidence that hunters facilitated lynx predation which resulted in superadditive mortality for deer during the hunting season. In environments with limited resources and time antipredator behavior may come at costs that ultimately affect prey demography. The strength of such non-consumptive effects (NCE) largely depends on the hunting mode of the predator. Ambush predators exert stronger NCE than active predators as they provide more persistent cues about predation risk than their widely roaming counterparts. While evidence comes from different taxonomic groups, evidence from large mammalian predator-prey systems is rare and equivocal. In the third chapter I looked at the occurrence and strength of NCE of the ambush predator, the Eurasian lynx on its main prey the European roe deer. According to ecological prediction I found that lynx evoked strong NCE that increased non-lynx-related mortality of roe deer in the study area. In summary, I focused in this thesis on identifying key drivers involved in shaping space use patterns of a large ambush predator and one of its main prey. Furthermore I characterized the spatiotemporal drivers of food acquisition and risk avoidance of both players in a human dominated environment. The presented results illustrate the tradeoffs between risk avoidance and food acquisition that affect decision making of all animals and show the potential costs of these tradeoffs in the form of reduced survival or reproduction. Finally the findings highlight the role of humans as ecological drivers of Ecosystem processes and may help developing efficient wildlife management plans that are needed for the conservation of large predators and their prey alike in human altered environements.

BackgroundThe process known as ecological diffusion emerges from a first principles view of animal movement, but ecological diffusion and other partial differential equation models can be difficult to fit to data. Step-selection functions (SSFs), on the other hand, have emerged as powerful practical tools for ecologists studying the movement and habitat selection of animals.MethodsSSFs typically involve comparing resources between a set of used and available points at each step in a sequence of observed positions. We use change of variables to show that ecological diffusion implies certain distributions for available steps that are more flexible than others commonly used. We then demonstrate advantages of these distributions with SSF models fit to data collected for a mountain lion in Colorado, USA.ResultsWe show that connections between ecological diffusion and SSFs imply a Rayleigh step-length distribution and uniform turning angle distribution, which can accommodate data collected at irregular time intervals. The results of fitting an SSF model with these distributions compared to a set of commonly used distributions revealed how precision and inference can vary between the two approaches.ConclusionsOur new continuous-time step-length distribution can be integrated into various forms of SSFs, making them applicable to data sets with irregular time intervals between successive animal locations.

Over the last decade, spatial capture–recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies — individual animals observed at specific locations and times — can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short‐term directional persistence), and habitat‐driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data‐augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual‐level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional “building blocks” of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.

Considering behavioral state when predicting habitat use: Behavior-specific spatial models for the endangered Tasmanian wedge-tailed eagle

BackgroundIdentifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals.MethodsUsing a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events.ResultsFor white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events.ConclusionsCaution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.

The Brownian bridge synoptic model of habitat selection and space use for animals using GPS telemetry data

Animal habitat selection is a process that functions at multiple, hierarchically. structured spatial scales. Thus multi-scale analyses should be the basis for inferences about factors driving the habitat selection process. Vertebrate herbivores forage selectively on the basis of phytochemistry, but few studies have investigated the influence of selective foraging (i.e., fine-scale habitat selection) on habitat selection at larger scales. We tested the hypothesis that phytochemistry is integral to the habitat selection process for vertebrate herbivores. We predicted that habitats selected at three spatial scales would be characterized by higher nutrient concentrations and lower concentrations of plant secondary metabolites (PSMs) than unused habitats. We used the Greater Sage-Grouse (Centrocercus urophasianus), an avian herbivore with a seasonally specialized diet of sagebrush, to test our hypothesis. Sage-Grouse selected a habitat type (black sagebrush, Artemisia nova) with lower PSM concentrations than the alternative (Wyoming big sagebrush, A. tridentata wyomingensis). Within black sagebrush habitat, Sage-Grouse selected patches and individual plants within those patches that were higher in nutrient concentrations and lower in PSM concentrations than those not used. Our results provide the first evidence for multi-scale habitat selection by an avian herbivore on the basis of phytochemistry, and they suggest that phytochemistry may be a fundamental driver of habitat selection for vertebrate herbivores.

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

Advances in biologging techniques and the availability of high‐resolution fisheries data have improved our ability to understand the interactions between seabirds and fisheries and to evaluate mortality risk due to bycatch. However, it remains unclear whether movement patterns and behaviour differ between birds foraging naturally or scavenging behind vessels and whether this could be diagnostic of fisheries interactions. We deployed novel loggers that record the GPS position of birds at sea and scan the surroundings to detect radar transmissions from vessels and immersion (activity) loggers on wandering albatrossesDiomedea exulansfrom South Georgia. We matched these data to remotely sensed fishing vessel positions and used a combination of hidden Markov and random forest models to investigate whether it was possible to detect a characteristic signature from the seabird tracking and activity data that would indicate fine‐scale vessel overlap and interactions. Including immersion data in our hidden Markov models allowed two distinct foraging behaviours to be identified, both indicative of Area Restricted Search (ARS) but with or without landing behaviour (likely prey capture attempts) that would not be detectable with location data alone. Birds approached vessels during all behavioural states, and there was no clear pattern associated with this type of scavenging behaviour. The random forest models had very low sensitivity, partly because foraging events at vessels occurred very rarely, and did not contain any diagnostic movement or activity pattern that was distinct from natural behaviours away from vessels. Thus, we were unable to predict accurately whether foraging bouts occurred in the vicinity of a fishing vessel, or naturally, based on behaviour alone. Our method provides a coherent and generalizable framework to segment trips using auxiliary biologging (immersion) data and to refine the classification of foraging strategies of seabirds. These results nevertheless underline the value of using radar detectors that detect vessel proximity or remotely sensed vessel locations for a better understanding of seabird–fishery interactions.

The white prawn, Fenneropenaeus indicus, and the tiger prawn, Penaeus monodon, are of great commercial importance, and they are extensively farmed along the eastern coast of India. Post-larvae of these species coexist in great abundance in estuaries along the Bay of Bengal. In this investigation, field distribution, habitat selection and stomach contents were analyzed in these two prawn species. Field sampling and laboratory studies revealed significant differences in their habitat and diet selection patterns of experimental populations. Post-larvae were collected bimonthly at two stations: (A) bare rocky flats and (B) sandy bottom bordered by mangrove vegetation. Station A was dominated by P. monodon (78.8%); station B was mostly represented by F. indicus (83.6%). Both species showed little variation in their field percentages during the sampling months between July and November 1996. In a laboratory setting, regardless of whether the species were single or mixed together, P. monodon selected rocky habitats (83%) over sand, and F. indicus showed strong selection for sandy substrate (86%). Stomach content analyses revealed phytoplankton to be the prawns' most abundant dietary item. Microalgal cells formed the major dietary component in F. indicus (60%), while P. monodon was found to consume primarily filamentous algae (65%). Such differential habitat and diet selection behavior may possibly reduce competition between these prawn species in the field. JOURNAL OF AQUATIC SCIENCES Volume 16, Number 2, October 2001, pp. 99-104 KEY WORDS: Fenneropenaeus, Penaeus, post-larvae, habitat, diet

Understanding how collective neuronal activity in the brain orchestrates behavior is a central question in integrative neuroscience. Addressing this question requires models that can offer a unified interpretation of multimodal data. In this study, we jointly examine video-recordings of zebrafish larvae freely exploring their environment and calcium imaging of the Anterior Rhombencephalic Turning Region (ARTR) circuit, which is known to control swimming orientation, recorded in vivo under tethered conditions. We show that both behavioral and neural data can be accurately modeled using a Hidden Markov Model (HMM) with three hidden states. In the context of behavior, the hidden states correspond to leftward, rightward, and forward swimming. The HMM robustly captures the key statistical features of the swimming motion, including bout-type persistence and its dependence on bath temperature, while also revealing inter-individual phenotypic variability. For neural data, the three states are found to correspond to left- and right-lateral activation of the ARTR circuit, known to govern the selection of left vs. right reorientation, and a balanced state, which likely corresponds to the behavioral forward state. To further unify the two analyses, we exploit the generative nature of the HMM, using neural sequences to generate synthetic swimming trajectories, whose statistical properties are similar to the behavioral data. Overall, this work demonstrates how state-space models can be used to link neuronal and behavioral data, providing insights into the mechanisms of self-generated action.

This paper highlights the need for intelligent analysis of students’ behavioral states in physical education tasks. The hand-ring inertial data is used to identify students’ motion sequence states. First, statistical feature extraction is performed based on the acceleration and angular velocity data collected from the bracelet. After completing the filtering and noise reduction of the data, we perform feature extraction by Back Propagation Neural Network (BPNN) and use the sliding window method for analysis. Finally, the classification capability of the model sequence is enhanced by the Hidden Markov Model (HMM). The experimental results indicate that the classification accuracy of student action sequences in physical education exceeds 96% after optimization by the HMM method. This provides intelligent means and new ideas for future student state recognition in physical education and teaching reform.