Temporal Movement Patterns Research Articles

Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework

Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets.

White-crested elaenias (Elaenia albiceps chilensis) breeding across Patagonia exhibit similar spatial and temporal movement patterns throughout the year.

For migratory birds, events happening during any period of their annual cycle can have strong carry-over effects on the subsequent periods. The strength of carry-over effects between non-breeding and breeding grounds can be shaped by the degree of migratory connectivity: whether or not individuals that breed together also migrate and/or spend the non-breeding season together. We assessed the annual cycle of the White-crested Elaenia (Elaenia albiceps chilensis), the longest-distance migrant flycatcher within South America, which breeds in Patagonia and spends the non-breeding season as far north as Amazonia. Using light-level geolocators, we tracked the annual movements of elaenias breeding on southern Patagonia and compared it with movements of elaenias breeding in northern Patagonia (1,365 km north) using Movebank Repository data. We found that elaenias breeding in southern Patagonia successively used two separate non-breeding regions while in their Brazilian non-breeding grounds, as already found for elaenias breeding in the northern Patagonia site. Elaenias breeding in both northern and southern Patagonia also showed high spread in their non-breeding grounds, high non-breeding overlap among individuals from both breeding sites, and similar migration phenology, all of which suggests weak migratory connectivity for this species. Elucidating the annual cycle of this species, with particular emphasis on females and juveniles, still requires further research across a wide expanse of South America. This information will be critical to understanding and possibly predicting this species' response to climate change and rapid land-use changes.

Bio-Inspired Small Target Motion Detection with Spatio-Temporal Feedback in Natural Scenes.

Small moving objects at far distance always occupy only one or a few pixels in image and exhibit extremely limited visual features, which bring great challenges to motion detection. Highly evolved visual systems endow flying insects with remarkable ability to pursue tiny mates and prey, providing a good template to develop image processing method for small target motion detection. The insects' excellent sensitivity to small moving objects is believed to come from a class of specific neurons called small target motion detectors (STMDs). However, existing STMD-based methods often experience performance degradation when coping with complex natural scenes. In this paper, we propose a bio-inspired visual system with spatio-temporal feedback mechanism (called Spatio-Temporal Feedback STMD) to suppress false positive background movement while enhancing system responses to small targets. Specifically, the proposed visual system is composed of two complementary subnetworks and a feedback loop. The first subnetwork is designed to extract spatial and temporal movement patterns of cluttered background by neuronal ensemble coding. The second subnetwork is developed to capture small target motion information where its output and signal from the first subnetwork are integrated together via the feedback loop to filter out background false positives in a recurrent manner. Experimental results demonstrate that the proposed spatio-temporal feedback visual system is more competitive than existing methods in discriminating small moving targets from complex natural environments.

Exploratory and seasonal movements of adult common shelducks in the eastern Wadden Sea

Habitat conditions surrounding individual birds are key components determining their fitness, notably by influencing effective foraging, escape from predators, and adaptation to changing environmental conditions. Birds living in dynamic habitats are particularly likely to have a high need to explore; however, spatial and temporal patterns of exploratory movements are poorly understood. We investigated the exploratory movements of adult common shelducks (hereafter Shelducks, Tadorna tadorna) in the Wadden Sea, which provides a dynamic habitat for birds. We captured nine shelducks in adjacent breeding areas and attached GPS satellite telemetry devices for 5–18 months, mostly covering the post-breeding period. We analyzed bird-movement patterns based on mean daily locations and behavioral change point analysis. All individuals explored on a median of 22% of tracking days (range = 6–39%). Individuals usually explored new areas for a short time before switching to stay in these areas for longer periods. Exploratory movements were particularly frequent before the start of the flightless period during molting. During this time, shelducks explored the traditional molting area in the Elbe estuary intensively. However, birds did not explore places within the area subsequently used for molting for longer or more often than other places. The molting area was used for a median of 59 days, i.e., about twice as long as required for the flightless period, suggesting that this area was explored not only to select a suitable molting place, but also for other purposes. Only two shelducks performed longer-distance exploration movements, i.e., > 45 km from their sedentary areas. We therefore concluded that exploratory movements by adult shelducks may enable the birds to adapt to small-scale changes in habitat conditions.

Body Image Within Temporal and Spatial Eye Movement Patterns

Body Image Within Temporal and Spatial Eye Movement Patterns

Dynamics and determinants of human–hyaena conflicts in the surroundings of a protected area

AbstractHuman–wildlife conflicts (HWC) predominate in areas proximal to wildlife rich protected zones. Whereas herbivore driven HWC is easy to detect, the stealth, nocturnal and predacious nature of carnivores leads to human injury and fatality, livestock depredation and loss of livelihoods in communities proximal to Sengwa Wildlife Research Area (SWRA) in northwest Zimbabwe. This study aimed to: (1) determine the nature of human–hyaena conflicts (HHCs), (2) explore the drivers of HHCs and (3) investigate the spatial and temporal movement patterns of hyaenas in HHC hotspots in areas proximal to SWRA. The results indicated that prevalent HHC types include livestock depredation, property damage, harassment, nuisance and disruption of human movements around SWRA. Locals indicated removal of park fences, human settlement on wildlife migration corridors, increases in hyaena populations, easy stray livestock targets, increased poaching and removal of natural wildlife death carcasses by illegal hunters as key drivers of HHC. Hyaenas have a predetermined non‐seasonal movement pattern following traditional wildlife migration corridors through SWRA into proximal communities. Human–hyaena conflict abatement strategies should be underpinned by indigenous local knowledge of hyaena movement and hunting patterns. Integrating citizen science data and remote sensing techniques aids to track and understand movement and hunting patterns of hyaenas inside and outside of protected areas.

Dynamics of creeping landslides controlled by inelastic hydro-mechanical couplings

Slow-moving landslides affect proximal infrastructures and communities, often causing extensive economic loss. While many of these landslides exhibit slow and episodic sliding for decades or more, they sometimes accelerate rapidly and fail catastrophically. Although it is known that the landslide dynamics are controlled by hydro-mechanical processes, few analytical models enable a versatile incorporation of the inelastic behavior of the shear zone materials, thus hindering an accurate quantification of how their properties modulate the magnitude and rate of coupled fluid flow and landslide motion. To address this problem, we develop a simulation framework incorporating rainfall-induced, deformation-mediated pore-water pressure transients at the base of active landslides. The framework involves the computation of two sequential diffusion processes, one within an upper rigid-porous landslide block, and another within the inelastic shear zone. Although the framework can be linked to any elastoplastic constitutive laws, here we model landslide motion through an elastic-perfectly plastic frictional model, which enables us to account for standard properties of earthen materials such as elastic moduli, friction angle, dilation angle, and hydraulic conductivity. Numerical case studies relevant to slow-moving landslides in the California Coast Ranges show that the proposed formulation captures different temporal patterns of movement induced by precipitation. In each of the case, we achieved a relatively accurate match between data and simulations by incorporating positive dilation coefficients, which leads to spontaneous generation of negative excess pore-water pressure and self-regulating motion. Conversely, simulations with no dilation (hence, reflecting the approach of critical state) produce sharp acceleration, typical of catastrophic runaway acceleration. Our findings encourage the use of the proposed framework in conjunction with constitutive laws tailored to site-specific geomaterial properties and data availability, thus favoring a versatile representation of the variety of creeping landslide trends observed in nature.

Multistate model to estimate sex‐specific dispersal rates and distances for a wetland‐breeding amphibian population

AbstractHow animals move across space and time in heterogeneous landscapes has important implications for the conservation of imperiled and dispersal‐limited taxa, such as amphibians. Wetland‐breeding amphibians are thought to exhibit strong site fidelity, but there is growing evidence that many species are more vagile than previously assumed. Few studies have quantified breeding dispersal probabilities or distances while also accounting for observational uncertainty, which means that resultant estimates could be biased or confounded with sampling intensity and other demographic rates (e.g., site‐ and time‐specific detection and survival probabilities). Here, we analyze data from a 6‐year capture–mark–recapture study on adult spotted salamanders (Ambystoma maculatum) conducted at 12 wetlands in central Pennsylvania to estimate population dynamics and population structure at multiple scales. We used a multistate, hidden Markov estimator to quantify sex‐specific site fidelity and breeding dispersal as a function of Euclidean distance between wetlands while accounting for imperfect detection. We estimated short‐timescale movements (i.e., those that occur within a single breeding season) and longer timescale movements (i.e., those that occur among breeding seasons) to determine if dispersal rates and distances differed. We found that interannual site fidelity of males varied among wetlands and was positively associated with population density. Females exhibited higher interannual site fidelity and dispersed further than males between breeding seasons. Within breeding seasons, we found that up to 6% of males dispersed to a new wetland each day. Our study is the first to directly compare amphibian breeding dispersal probabilities and distances at multiple scales and provides a robust framework for improving inference on the spatial and temporal patterns of amphibian movement.

Hawaiian waterbird movement across a developed landscape

AbstractA key component for biologists managing mobile species is understanding where and when a species occurs at different locations and scaling management to fit the spatial and temporal patterns of movement. We established an automated radio‐telemetry tracking network to document multi‐year movement in 2016–2018 of 3 endangered waterbirds among wetlands on Oʻahu, Hawaiʻi, USA: ʻalae ʻula or Hawaiian gallinule (gallinule; Gallinula galeata sandvicensis), ʻalae keʻokeʻo or Hawaiian coot (coot; Fulica alai), and aeʻo or Hawaiian stilt (stilt; Himantopus mexicanus knudseni), each with different ecological requirements. There were marked differences in the movement propensity of the species, with no movement among sites detected in gallinules, 31% of coots moving among wetlands, and very high levels of daily movement in stilts. A network analysis revealed strong evidence for fidelity among individual stilts to specific wetlands, indicating different groups of wetlands supported different birds. There was also strong evidence for patterns in daily and seasonal movement patterns of stilts. Our work indicates the importance of each wetland to the waterbirds they support, as each individual had strong fidelity to a single wetland. In addition, for Hawaiian coots and stilts, which were documented moving among multiple wetlands, a network of wetlands may be key for long‐term persistence of these endangered species, and coordinated regional management of waterbirds as a shared resource could provide greater benefits to waterbirds than independent management of each wetland.

Migration Responses to the COVID-19 Pandemic: A Case Study of New England Showing Movements down the Urban Hierarchy and Ensuing Impacts on Real Estate Markets

As the COVID-19 pandemic swept across the United States, the media began reporting stories of people leaving cities for rural destinations, setting off frenzied real estate activity in receiving communities. This article builds a case study of New England using nonconventional data collected from mobile devices as a proxy for population to explore the temporal and spatial patterns of movement down the urban hierarchy since the onset of the COVID-19 pandemic. Two research questions guide the analysis: (1) How have urban–rural migration systems in New England shifted since the onset of the COVID-19 pandemic? (2) In what ways have real estate markets been affected by these apparent migration shifts? The analysis reveals shifts of population away from metropolitan core areas of the region and into micropolitan and noncore counties. These population shifts were most pronounced in late summer and fall 2020 with loosening travel restrictions. By the end of 2020, migration systems in New England once more resembled prepandemic patterns. Further, these places down the urban hierarchy consistently showed more substantial increases in real estate activity as reflected in rising prices, reduced inventories, and increased sales volume. These real estate dynamics suggest urban to rural migration during the COVID-19 pandemic might be initiating new waves of rural gentrification.

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.

PurposeNocturnal sounds contain numerous information and are easily obtainable by a non-contact manner. Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones.Patients and MethodsTwo different audio datasets were used: audio data routinely recorded by a solitary microphone chip during polysomnography (PSG dataset, N=1154) and audio data recorded by a smartphone (smartphone dataset, N=327). The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement from ambient noise. The proposed neural network model learns to first extract features from each 30-second epoch and then analyze inter-epoch relationships of extracted features to finally classify the epochs into sleep stages.ResultsOur model achieved 70% epoch-by-epoch agreement for 4-class (wake, light, deep, REM) sleep stage classification and robust performance across various signal-to-noise conditions. The model performance was not considerably affected by sleep apnea or periodic limb movement. External validation with smartphone dataset also showed 68% epoch-by-epoch agreement.ConclusionThe proposed end-to-end deep learning model shows potential of low-quality sounds recorded from microphone chips to be utilized for sleep staging. Future study using nocturnal sounds recorded from mobile devices at home environment may further confirm the use of mobile device recording as an at-home sleep tracker.

Similarity in Temporal Movement Patterns in Laying Hens Increases with Time and Social Association.

Simple SummarySocial bonds are well-known to affect individual decisions or responses in specific situations, however, the impact of sociality on the individual activity behaviour of hens is largely unexplored. Therefore, we explored the relationship between social associations and individual daily movement patterns by studying the range use of domestic hens in a semi-commercial barn setting with automatic tracking of laying hens. We analysed the social relationships and the daily movement patterns of hens and found that hens were consistent in their individual variation in daily movement patterns and maintained stable social relationships. The social associations among hens correlated with movement patterns and this correlation increased with time, leading to more similar movement patterns of socially associated individuals. This study clearly shows that the social environment within a group can shape and enhance variation in movement patterns of individual animals.We explored the relationship between social associations and individual activity patterns in domestic hens. Out of 1420 laying hens, 421 hens were equipped with RFID tags attached to RFID-specific leg bands (leg bands from Company Roxan, Selkirk, Scotland) to continuously track their change in location across four different areas (one indoor and three outdoor areas). Using a combination of social network analysis for quantifying social relationships and dynamic time warping for characterizing the movement patterns of hens, we found that hens were consistent in their individual variation in temporal activity and maintained stable social relationships in terms of preferred association partners. In addition to being consistent, social associations correlated with movement patterns and this correlation strengthened over the period of observation, suggesting that the animals aligned their activity patterns with those of their social affiliates. These results demonstrate the importance of social relationships when considering the expression of individual behaviour. Notably, differences in temporal patterns emerge despite rather homogeneous rearing conditions, same environment, and low genetic diversity. Thus, while variation in behavioural phenotypes can be observed across isolated individuals, this study shows that the social environment within a group can shape and enhance variation in general movement patterns of individual animals.

An Indoor Crowd Movement Trajectory Benchmark Dataset

In recent years, technologies of indoor crowd positioning and movement data analysis have received widespread attention in the fields of reliability management, indoor navigation, and crowd behavior monitoring. However, only a few indoor crowd movement trajectory datasets are available to the public, thus restricting the development of related research and application. This article contributes a new benchmark dataset of indoor crowd movement trajectories. This dataset records the movements of over 5000 participants at a three-day large academic conference in a two-story indoor venue. The conference comprises varied activities, such as academic seminars, business exhibitions, a hacking contest, interviews, tea breaks, and a banquet. The participants are divided into seven types according to participation permission to the activities. Some of them are involved in anomalous events, such as loss of items, unauthorized accesses, and equipment failures, forming a variety of spatial–temporal movement patterns. In this article, we first introduce the scenario design, entity and behavior modeling, and data generator of the dataset. Then, a detailed ground truth of the dataset is presented. Finally, we describe the process and experience of applying the dataset to the contest of ChinaVis Data Challenge 2019. Evaluation results of the 75 contest entries and the feedback from 359 contestants demonstrate that the dataset has satisfactory completeness, and usability, and can effectively identify the performance of methods, technologies, and systems for indoor trajectory analysis.

Seasonality in daily movement patterns of mandrills revealed by combining direct tracking and camera traps.

Movement is a fundamental characteristic of animals, but challenging to measure noninvasively. Noninvasive methods for measuring travel have different weaknesses, so multiple techniques need to be applied multiple techniques for reliable inferences. We used two methods, direct tracking and camera trapping, to examine the variation in time and seasonal differences in movement rates of mandrills (Mandrillus sphinx), an elusive primate that lives in large groups in central Africa. In a 400-km2 rainforest area in Moukalaba-Doudou National Park, Gabon, we tracked unidentified groups 46 times from 2009 to 2013. We systematically placed 157 terrestrial camera traps in the same area from 2012 to 2014 and recorded groups 309 times. Generalized additive mixed models (GAMMs) of the tracking data indicated that the group travel speed varied with time and season. In the fruiting season, the movement rate fluctuated with time in a bimodal pattern, whereas in the nonfruiting season, it increased monotonously with time. The predicted day range was longer in the fruiting season (6.98 km) than in the nonfruiting season (6.06 km). These seasonal differences suggest responses to changes in food resources and temperature. Camera-trap detection rates showed similar temporal and seasonal patterns to the tracking data, allowing us to generalize our findings to the population level. Moreover, cameras never detected mandrills at night, and we observed that they slept high in trees and hardly moved until the next morning, all suggesting their strict avoidance of nighttime movement. This study demonstrated the significance of the multiple-method approach in drawing robust conclusions on temporal patterns of animal movement.

Spatio-temporal mobility patterns of on-demand ride-hailing service users

ABSTRACT Understanding individual mobility behavior is critical for modeling urban transportation. Different types of emerging data sources such as mobile phone records, social media posts, GPS observations, and smart card transactions have been used to reveal individual mobility behavior. In this paper, spatio-temporal mobility behaviors are reported using large-scale data collected from a ride-hailing service platform. Using passenger-level travel information, to characterize temporal movement patterns, trip generation characteristics, and distribution of gap time between consecutive trips are revealed. To understand spatial mobility patterns, we observe the spatial distribution of residences and workplaces, and the distributions of travel distance and travel time. Our analysis highlights the differences in mobility patterns of ride-hailing services users, compared to the findings of existing studies based on other data sources. The results show the potential of developing high-resolution individual-level mobility models that can predict the demand for emerging mobility services with high fidelity and accuracy.

Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users' travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers' residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns.

Spatial and temporal responses of swamp wallabies to roads in a human-modified landscape

Roads can have significant impacts on wildlife populations by impeding movement, restricting access to resources and causing wildlife–vehicle collisions. In particular, wildlife–vehicle collisions represent a substantial conservation and social problem, and although mitigation measures are available, an increased understanding of the temporal and spatial patterns of animal movement around roads will enhance their effectiveness. We analysed GPS telemetry data from 47 swamp wallabies Wallabia bicolor on Phillip Island, south-east Australia, within patches of native vegetation dissected by roads. Our aims were to determine if (a) road crossing frequency was influenced by time period (day, night) or sex, (b) wallabies avoided roads, and if avoidance was influenced by time period or sex and (c) road crossing locations were associated with dense vegetation, and other habitat characteristics. We found that males crossed roads more often at night than during the day while females showed the opposite pattern. Further, wallabies avoided roads, with some evidence that avoidance increased at night (p = 0.07). The chance of a wallaby crossing roads with high speed limits (80–100 km h-1) increased with vegetation density during the day but not at night. In contrast, vegetation density had no influence on crossing locations along roads with lower (50–70 km h-1) speed limits during the day or night. Both vegetation density and vehicle speed may influence wallaby–vehicle collisions and suggest management strategies targeting these factors. Partial fencing guiding wallabies towards safer crossing locations, combined with other measures such as reduced speed limits and signage, could reduce collisions between vehicles and swamp wallabies on Phillip Island.

Interpretable Self-Supervised Facial Micro-Expression Learning to Predict Cognitive State and Neurological Disorders

Human behavior is the confluence of output from voluntary and involuntary motor systems. The neural activities that mediate behavior, from individual cells to distributed networks, are in a state of constant flux. Artificial intelligence (AI) research over the past decade shows that behavior, in the form of facial muscle activity, can reveal information about fleeting voluntary and involuntary motor system activity related to emotion, pain, and deception. However, the AI algorithms often lack an explanation for their decisions, and learning meaningful representations requires large datasets labeled by a subject-matter expert. Motivated by the success of using facial muscle movements to classify brain states and the importance of learning from small amounts of data, we propose an explainable self-supervised representation-learning paradigm that learns meaningful temporal facial muscle movement patterns from limited samples. We validate our methodology by carrying out comprehensive empirical study to predict future speech behavior in a real-world dataset of adults who stutter (AWS). Our explainability study found facial muscle movements around the eyes (p

Automatic detection and classification of baleen whale social calls using convolutional neural networks.

Passive acoustic monitoring has proven to be an indispensable tool for many aspects of baleen whale research. Manual detection of whale calls on these large data sets demands extensive manual labor. Automated whale call detectors offer a more efficient approach and have been developed for many species and call types. However, calls with a large level of variability such as fin whale (Balaenoptera physalus) 40 Hz call and blue whale (B. musculus) D call have been challenging to detect automatically and hence no practical automated detector exists for these two call types. Using a modular approach consisting of faster region-based convolutional neural network followed by a convolutional neural network, we have created automated detectors for 40 Hz calls and D calls. Both detectors were tested on recordings with high- and low density of calls and, when selecting for detections with high classification scores, they were shown to have precision ranging from 54% to 57% with recall ranging from 72% to 78% for 40 Hz and precision ranging from 62% to 64% with recall ranging from 70 to 73% for D calls. As these two call types are produced by both sexes, using them in long-term studies would remove sex-bias in estimates of temporal presence and movement patterns.

Migration strategy, site fidelity and population size of the globally threatened Sociable Lapwing Vanellus gregarius

Population declines of the critically endangered Sociable Lapwing are probably due to high mortality along its migration routes or on its wintering grounds, both of which are very poorly known. We therefore undertook a long-term study of the species’ movements using satellite tagging, colour-ringing, targeted field surveys and a database of historical and recent sightings. We identified two migration flyways from the breeding grounds in Kazakhstan; a longer western route (c. 5200 km) west through southern Russia, then south through the Caucasus to wintering areas in Saudi Arabia and eastern Sudan, and a shorter eastern route (c. 2800 km) south through Turkmenistan and Uzbekistan to wintering areas in Pakistan and north-western India. The migration strategy is characterised by infrequent long-distance movements followed by often lengthy stopovers in a small number of staging areas that are used consistently across years, and by high individual and low between-individual consistency in spatial and temporal patterns of movement. At least four main autumn stopover areas and one additional spring stopover area were identified along the western route, but only one autumn and one spring staging area along the eastern route. There was no relationship between latitude or longitude of breeding and the migration route used, and the same breeding colonies could contain breeding adults and produce chicks of both flyway populations, suggesting that no clear migratory divide exists within the breeding range. Sociable Lapwings spend around a third of the year on their breeding grounds, a third on their wintering grounds and a third moving between them. Birds were highly faithful to passage and wintering sites, but showed low fidelity to breeding sites. Stopover areas and wintering sites are usually located at the interface of dry steppe-like or desert habitats and agriculture, particularly irrigated cropland along rivers. This species selects agricultural habitats throughout its entire life cycle, and its heavy reliance on some of the world’s most anciently cultivated regions suggests that this synanthropic relationship may have evolved over millennia. The recent emergence of irrigated agriculture in the deserts of Arabia allows birds using the western route to winter far north of their previous wintering range and maybe to spread into new wintering areas along the coasts of the Arabian Gulf. The concentration of large numbers of birds at a small number of traditional stopover areas offers the opportunity to quantify the global population size, which we estimate at around 24,000 individuals (95% CL 13,700–55,560). However, it also makes the species particularly vulnerable to hunting and small-scale habitat change. Illegal hunting along the western flyway is identified as the most likely driver of the species’ decline.