Analysis Of Movement Patterns Research Articles

Determining what expert piano sight-readers have in common

ABSTRACT Music sight-reading is a valuable skill that eludes and frustrates many musicians. Techniques for teaching sight-reading are varied, with teachers mostly falling back on personal experience or simply hoping that, somehow, the penny will drop for the student. This study reports on a survey of the music learning and playing habits of expert and non-expert piano sight-readers. Pianists were categorised as ‘experts’ according to their ability to perfectly perform a 6th Grade AMEB (Australian Music Examinations Board) sight-reading assessment piece. This grouping was determined by the analysis of eye movement patterns as pianists performed various sight-reading tasks (Arthur 2017). The data show significant differences in musical training and performance experiences between the two groups.

An exploratory analysis of demography and movement patterns of dogs: New insights in the ecology of endemic Rocky Mountain-Spotted Feverin Mexicali, Mexico.

An outbreak of Rocky Mountain spotted fever (RMSF) transmitted by the brown dog tick (Rhipicephalus sanguineus sensu lato) has emerged as a major human and animal health concern in Mexicali, Mexico. Due to high rates of brown dog tick infestation, susceptibility, and association with humans, dogs serve as sentinels and have a key role in the ecology of RMSF. A cross-sectional household questionnaire study was conducted in six rural and urban locations to characterize dog ecology and demography in RMSF high-and low-risk areas of Mexicali. In addition, we tracked movement patterns of 16 dogs using a GPS data logger. Of 253 households, 73% owned dogs, and dog ownership tended to be higher in high-risk areas, with a mean dog:human ratio of 0.43, compared with 0.3 in low-risk areas. Dogs in high-risk areas had higher fecundity and roamed more, but the dog density and numbers of free-roaming dogs were comparable. There was a higher proportion of younger dogs and lower proportion of older dogs in high-risk areas. The high proportion of immunologically naïve puppies in high risk areas could result in a lack of herd immunity leading to a more vulnerable dog and human population. The marked increase of space use of free-roaming dogs in high-risk areas suggests that unrestrained dogs could play an important role in spreading ticks and pathogens. As means to limit RMSF risk, practical changes could include increased efforts for spay-neuter and policies encouraging dog restraint to limit canine roaming and spread of ticks across communities; due to dog density is less impactful such policies may be more useful than restrictions on the number of owned dogs.

A time-geographic approach to identifying daily habitat use patterns for Amazonian Black Skimmers

Analysis of movement patterns at fine temporal scales may suggest how animals interact with their surrounding habitat. This is particularly useful in determining not only where animals may spend their time, but when and how likely they may be to interact with or utilize specific habitat resources. While previous studies demonstrate various methods seeking to address this concern, most apply for a single individual or a single point pair in a trajectory. However, for practical conservation concerns, information on animal-habitat interactions should be considered at the group level. This paper applies two approaches from movement analysis, the probabilistic voxel-based space-time prism and the comprehensive probability surface, to construct utilization distributions corresponding to specific intervals in time. Utilization distributions are processed towards summary quantification of diel habitat interaction probabilities for multiple individuals. We evaluate this approach using primary tracking data collected from Black skimmers (Rynchops niger cinerascens) traversing Manu National Park, Peru and surrounding areas. Habitat interaction graphs were constructed summarizing animal interaction by habitat type and time of day for all individuals tracked. This information was used to corroborate known black skimmer habitat use behaviors. Habitat interaction results were generally consistent with skimmers’ known foraging patterns for dawn, dusk and overnight hours. Results also raised a noteworthy use pattern for urban areas at dusk and early evening otherwise unobserved. Findings suggest applicability of this approach for cryptic species where direct observation is difficult, but trajectory data are available. Utility of this method for the practical work of conservationists and related researchers is discussed.

Enumeration strategy differences revealed by saccade-terminated eye tracking

Brain regions involved in saccadic eye movements partially overlap with a frontoparietal network implicated in encoding numerosities. Eye movement patterns may plausibly reflect strategic scanning behaviours to resolve the open-ended task of efficiently enumerating visual arrays. If so, these patterns may help explain individual differences in enumeration acuity in terms of well-understood visual attention mechanisms. Most enumeration eye-tracking paradigms, however, do not allow for direct manipulation of eye movement behaviours to test these claims. In the current study we terminated trials after a specified number of saccades to systematically probe the time course of enumeration strategies. Fifteen adults (11 naïve, 4 informed) enumerated random dot arrays under three conditions: (1) a novel saccade-terminated design where arrays were visible until one, two or four saccades had occurred; (2) a duration-terminated design where arrays were shown for 250, 500 or 1000 ms; and (3) a response-terminated design where arrays were visible until a response. Participants gave more accurate responses when enumerating saccade-terminated trials despite taking a similar time as in the duration-terminated trials. When participants were informed how trials would terminate, their saccade onset latencies shifted to match task demands. Rotating saccade vectors to align with salient image locations accounted for variability in the orientation of saccade trajectories. These findings (1) show a combination of stimulus-derived visual processing and task-based strategic demands account for enumeration eye movements patterns, (2) validate a novel saccade-contingent trial termination procedure for studying sequences of enumeration eye movements, and (3) highlight the need to include analyses of spatial and temporal eye movement patterns into models of visual enumeration strategies.

Movement Patterns of the Eastern Chipmunk (<i>Tamias striatus</i>) in Four Fragmented Landscapes of Prince Edward Island, Canada

The analysis of animal movement patterns can provide important information on animals’ responses to habitat features. In this study, the movement paths of eastern chipmunks (Tamias striatus) were examined in four landscapes, with different levels of habitat fragmentation, using either fluorescent powdering or spool-and-line tracking. Descriptions of the tree and ground vegetation communities were performed in the vicinity of the trail to obtain information on habitat use and habitat selection. Several key movement variables were calculated, including the total path length, net distance, fractal dimension, and radius of gyration. Despite statistically significant differences in some of the movement metrics between the four landscapes, the overall movement patterns were generically the same for all of chipmunk paths examined in this study. The data were compared to trends expected based on random or correlated random walks, as well as Lévy-walk models. The mean squared net displacement did not support the correlated random walk predictions, except at smaller spatial scales, but overall demonstrated Lévy-like super diffusive behaviour. Lévy-like patterns were also confirmed from the move-length distributions that demonstrated truncated-tail power-law behaviour. Although this would suggest invariance of the movement patterns at all spatial scales studied, fractal analysis revealed at least two transitions in movement patterns at scales of around 2 and 5 m. The transition point at 2 m was negatively correlated with the density of small trees, while the transition at ~5 m was positively correlated with the spatial distribution of large trees. As the habitat-preference data showed that small trees are among the least preferred habitat component, while large trees were among the most preferred habitat, chipmunks are likely to alter their movement behaviour to avoid small trees, and attracted towards large trees possibly to avoid predators. Overall, we determined three principal domains of movement: at smaller spatio-temporal scales, foraging activities dominate and the movement is highly correlated but also random; at intermediate spatial scales, chipmunks may be moving to avoid predators, using different environmental cues, and the movement is more directed (but still influenced by vegetation patterns at intermediate scales); at larger spatio-temporal scales, the movement is dominated by long-range/long-term memory and homing to burrows and other key habitat features, such as food caches, drives more directed movement. The fact that scale-dependent movement mechanisms could give rise to LW patterns is consistent with recent studies.

ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user's movement patterns that emerge from inertial sensors data. While the literature on this topic is quite mature, existing approaches are still not adequate to discriminate activities characterized by similar physical movements. The context that surrounds the user (e.g., semantic location) could be used as additional information to significantly extend the set of recognizable activities. Since collecting a comprehensive training set with activities performed in every possible context condition is too costly, if possible at all, existing works proposed knowledge-based reasoning over ontological representation of context data to refine the predictions obtained from machine learning. A problem with this approach is the rigidity of the underlying logic formalism that cannot capture the intrinsic uncertainty of the relationships between activities and context. In this work, we propose a novel activity recognition method that combines semi-supervised learning and probabilistic ontological reasoning. We model the relationships between activities and context as a combination of soft and hard ontological axioms. For each activity, we use a probabilistic ontology to compute its compatibility with the current context conditions. The output of probabilistic semantic reasoning is combined with the output of a machine learning classifier based on inertial sensor data to obtain the most likely activity performed by the user. The evaluation of our system on a dataset with 13 types of activities performed by 26 subjects shows that our probabilistic framework outperforms both a pure machine learning approach and previous hybrid approaches based on classic ontological reasoning.

Computational Analysis of Movement Patterns of Dogs with ADHD-Like Behavior.

Simple SummaryADHD-like (attention deficit hyperactivity disorder) behavior in dogs may be expressed as impulsivity, inattentiveness, or aggression, compromising both dog and owner quality of life. Its treatment in a clinical setting requires behavioral modification and sometimes a medical treatment is added. There is a lack of objective tools for assessment and diagnosis of the problem, and behavioral experts mostly rely on owner reports. To address this gap, in this paper we use a self-developed computational tool which automatically analyzes movement of a dog from video footage collected during behavioral consultation. Based on a computational analysis of behavioral consultations of 12 dogs medically treated due to ADHD-like behavior and of a control group of 12 dogs with no reported behavioral problems, we identify three dimensions of characteristic movement patterns of dogs with ADHD-like behaviors, which are detectable during consultation. These include (i) high speed of movement, (ii) large coverage of room space, and (iii) frequent re-orientation in room space. These patterns can form the basis for computational methods for objective assessment of dogs with ADHD-like behavior that could help for diagnosis and clinical treatment of the disorder.Computational approaches were called for to address the challenges of more objective behavior assessment which would be less reliant on owner reports. This study aims to use computational analysis for investigating a hypothesis that dogs with ADHD-like (attention deficit hyperactivity disorder) behavior exhibit characteristic movement patterns directly observable during veterinary consultation. Behavioral consultations of 12 dogs medically treated due to ADHD-like behavior were recorded, as well as of a control group of 12 dogs with no reported behavioral problems. Computational analysis with a self-developed tool based on computer vision and machine learning was performed, analyzing 12 movement parameters that can be extracted from automatic dog tracking data. Significant differences in seven movement parameters were found, which led to the identification of three dimensions of movement patterns which may be instrumental for more objective assessment of ADHD-like behavior by clinicians, while being directly observable during consultation. These include (i) high speed, (ii) large coverage of space, and (iii) constant re-orientation in space. Computational tools used on video data collected during consultation have the potential to support quantifiable assessment of ADHD-like behavior informed by the identified dimensions.

Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business

Retailers need accurate movement pattern analysis of human-tracking data to maximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers’ movement patterns and their relationships with sales. In this research, we adopt indoor positioning technology, including wireless sensor devices and fingerprinting techniques, to track customers’ movement patterns in a fashion retail store over four months. Specifically, we conducted three field experiments in three different timeframes. In each experiment, we rearranged one element of the visual merchandising display (VMD) to track and compare customer movement patterns before and after the rearrangement. For the analysis, we connected customers’ discrete location data to identify meaningful patterns in customers’ movements. We also used customers’ location and time information to match identified movement pattern data with sales data. After classifying individuals’ movements by time and sequences, we found that stay time in a particular zone had a greater impact on sales than the total stay time in the store. These results challenge previous findings in the literature that suggest that the longer customers stayed in a store, the more they purchase. Further, the results confirmed that effective store rearrangement could change not only customer movement patterns but also overall sales of store zones. This research can be a foundation for various practical applications of tracking data technologies.

INFLUENCE OF MASS ‘U’ FORM IN LINE UP FORMATION TOWARDS AIR MOVEMENT ON SUPPORTING THERMAL COMFORT IN OUTSIDE SPACE OF APARTMENT LANDMARK RESIDENCE, BANDUNG

Abstract- Indonesia with the characteristics of tropical climate requires control in design to achieve thermal comfort both in outside space and inside space. One of the factors that can affect thermal comfort is the pattern of air movement and air velocity. Landmark Residence Apartment is one of the new apartments in Bandung, with mass ‘U’ form in line up of new partially awakened. The newly constructed 'U' mass formation forms the characteristic of the two different masses, the space side in the closed A mass with the characteristic of the centered space as well as the side in the open mass B. this apartment become an object of research to determine the factors of order and mass form to the characteristics of air movement as well as thermal comfort in the outside space as a space of activities in residents. The method used in this research is descriptive-quantitative with simulation software Flow Design to know the air movement and measurement of thermal comfort factor at the point of population and sample which have been determined. This thermal factor will be analyzed using CET Nomogram method, so it generates thermal comfort level in outer space which will be associated with order and mass form of apartment. By using analysis of air movement patterns and CET Nomogram, it is seen that thermal comfort level in the outside space Landmark Residence apartment is more affected by wind velocity. The mass 'U' form with the back to wind direction will produce high wind velocity on the outside space of the building's side, while on the inner side of the 'U' form will form the shadow of the wind and the pattern of turbulence air movement affecting the achievement of different thermal conditions. The 'U' mass sequence lined up can also cause air movement on the inner side of closed A mass not getting enough air movement compared to open mass B with better airflow pattern. The pattern of air movement on the inner side of mass A can be at any time higher when the wind velocity enters the gap between the masses. Key Words: apartment, outside space, mass ‘U’ form in line up, air movement, thermal comfort.

Visual analytics of taxi trajectory data via topical sub-trajectories

GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning. Topic modeling is an effective tool to extract semantic information from taxi trajectory data. However, previous methods generally ignore trajectory directions that are important in the analysis of movement patterns. In this paper, we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories. We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic. Finally, we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories. The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system.

Texts and pictures serve different functions in conjoint mental model construction and adaptation.

In this study we examined the different functions of text and pictures during text-picture integration in multimedia learning. In Study 1, 144 secondary school students (age = 11 to 14 years; 72 females, 72 males) received six text-picture units under two conditions. In the delayed-question condition, students first read the units without a specific question (no-question phase), to stimulate initial coherence-oriented mental model construction. Afterward the question was presented (question-answering phase), to stimulate task-adaptive mental model specification. In the preposed-question condition, students received a specific question from the beginning, stimulating both kinds of processing. Analyses of the participants' eye movement patterns confirmed the assumption that students allocated a higher percentage of available resources to text processing during the initial mental model construction than during adaptive model specification. Conversely, students allocated a higher percentage of available resources to picture processing during adaptive mental model specification than during the initial mental model construction. In Study 2 (N = 12, age = 12 to 16; seven females, five males), we ruled out that these findings were due to the effect of rereading, by implementing a no-question phase either once or twice. To sum up, texts seem to provide more explicit conceptual guidance in mental model construction than pictures do, whereas pictures support mental model adaptation more than text does, by providing flexible access to specific information for task-oriented updates.

A Data Science Framework for Movement

Movement is the driving force behind the form and function of many ecological and human systems. Identification and analysis of movement patterns that may relate to the behavior of individuals and their interactions is a fundamental first step in understanding these systems. With advances in IoT and the ubiquity of smart connected sensors to collect movement and contextual data, we now have access to a wealth of geo‐enriched high‐resolution tracking data. These data promise new forms of knowledge and insight into movement of humans, animals, and goods, and hence can increase our understanding of complex spatiotemporal processes such as disease outbreak, urban mobility, migration, and human‐species interaction. To take advantage of the evolution in our data, we need a revolution in how we visualize, model, and analyze movement as a multidimensional process that involves space, time, and context. This paper introduces a data science paradigm with the aim of advancing research on movement.

System level analysis of motor-related neural activities in larval Drosophila

The way in which the central nervous system (CNS) governs animal movement is complex and difficult to solve solely by the analyses of muscle movement patterns. We tackle this problem by observing the activity of a large population of neurons in the CNS of larval Drosophila. We focused on two major behaviors of the larvae – forward and backward locomotion – and analyzed the neuronal activity related to these behaviors during the fictive locomotion that occurs spontaneously in the isolated CNS. We expressed a genetically-encoded calcium indicator, GCaMP and a nuclear marker in all neurons and then used digitally scanned light-sheet microscopy to record (at a fast frame rate) neural activities in the entire ventral nerve cord (VNC). We developed image processing tools that automatically detected the cell position based on the nuclear staining and allocate the activity signals to each detected cell. We also applied a machine learning-based method that we recently developed to assign motor status in each time frame. Our experimental procedures and computational pipeline enabled systematic identification of neurons that showed characteristic motor activities in larval Drosophila. We found cells whose activity was biased toward forward locomotion and others biased toward backward locomotion. In particular, we identified neurons near the boundary of the subesophageal zone (SEZ) and thoracic neuromeres, which were strongly active during an early phase of backward but not forward fictive locomotion.

Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

How Shall I Count the Ways? A Method for Quantifying the Qualitative Aspects of Unscripted Movement With Laban Movement Analysis.

There is significant clinical evidence showing that creative and expressive movement processes involved in dance/movement therapy (DMT) enhance psycho-social well-being. Yet, because movement is a complex phenomenon, statistically validating which aspects of movement change during interventions or lead to significant positive therapeutic outcomes is challenging because movement has multiple, overlapping variables appearing in unique patterns in different individuals and situations. One factor contributing to the therapeutic effects of DMT is movement’s effect on clients’ emotional states. Our previous study identified sets of movement variables which, when executed, enhanced specific emotions. In this paper, we describe how we selected movement variables for statistical analysis in that study, using a multi-stage methodology to identify, reduce, code, and quantify the multitude of variables present in unscripted movement. We suggest a set of procedures for using Laban Movement Analysis (LMA)-described movement variables as research data. Our study used LMA, an internationally accepted comprehensive system for movement analysis, and a primary DMT clinical assessment tool for describing movement. We began with Davis’s (1970) three-stepped protocol for analyzing movement patterns and identifying the most important variables: (1) We repeatedly observed video samples of validated (Atkinson et al., 2004) emotional expressions to identify prevalent movement variables, eliminating variables appearing minimally or absent. (2) We use the criteria repetition, frequency, duration and emphasis to eliminate additional variables. (3) For each emotion, we analyzed motor expression variations to discover how variables cluster: first, by observing ten movement samples of each emotion to identify variables common to all samples; second, by qualitative analysis of the two best-recognized samples to determine if phrasing, duration or relationship among variables was significant. We added three new steps to this protocol: (4) we created Motifs (LMA symbols) combining movement variables extracted in steps 1–3; (5) we asked participants in the pilot study to move these combinations and quantify their emotional experience. Based on the results of the pilot study, we eliminated more variables; (6) we quantified the remaining variables’ prevalence in each Motif for statistical analysis that examined which variables enhanced each emotion. We posit that our method successfully quantified unscripted movement data for statistical analysis.

Common bottlenose dolphin (Tursiops truncatus) behavior in an active narrow seaport.

The Galveston Ship Channel (GSC) is a narrow, congested waterway that supports large-scale shipping, commercial fishing, dolphin tourism, and recreation. Human activity and common bottlenose dolphins (Tursiops truncatus) converge in the GSC with potentially negative consequences on the dolphins. Elevated land-based tracking and behavioral observation of dolphins and vessels were conducted along the GSC in June-August 2013 using a digital theodolite. Positional information was used to calculate dolphin movement patterns and proximity to vessels. Log-likelihood ratio and Chi-square contingency tests were used to assess behavioral states, and generalized additive models were used to analyze movement patterns (i.e., swimming speed, reorientation rate, and linearity) relative to endogenous and exogenous factors and vessel presence. Dolphins regularly use the GSC to forage (57% of observed behavioral states) and socialize (27%), and it is not a travel corridor for accessing other favorable sites (traveling = 5%). Dolphin behavior varied significantly based on time of day, group size, calf presence, and general boat presence. When boats were present, the proportion of time dolphins spent socializing and foraging was significantly less than expected by chance. Swimming speeds increased significantly in the presence of small recreational boats, dolphin-watching tour boats, shrimp trawlers, and when tour boats and shrimp trawlers were both present. Reorientation rate increased significantly in the presence of tour boats and trawlers. Dolphin behavioral responses to vessel presence may result in decreased energy consumption due to disrupted foraging activity. Without proper management, the observed behavioral changes may be detrimental to individuals within this population in the short term, with potential long-term consequences to health and survivorship.

Parsimonious test of dynamic interaction.

In recent years, there have been significant advances in the technology used to collect data on the movement and activity patterns of humans and animals. GPS units, which form the primary source of location data, have become cheaper, more accurate, lighter and less power‐hungry, and their accuracy has been further improved with the addition of inertial measurement units. The consequence is a glut of geospatial time series data, recorded at rates that range from one position fix every several hours (to maximize system lifetime) to ten fixes per second (in high dynamic situations). Since data of this quality and volume have only recently become available, the analytical methods to extract behavioral information from raw position data are at an early stage of development. An instance of this lies in the analysis of animal movement patterns. When investigating solitary animals, the timing and location of instances of avoidance and association are important behavioral markers. In this paper, a novel analytical method to detect avoidance and association between individuals is proposed; unlike existing methods, assumptions about the shape of the territories or the nature of individual movement are not needed. Simulations demonstrate that false positives (type I error) are rare (1%–3%), which means that the test rarely suggests that there is an association if there is none.

POLA PERGERAKAN “PASAR TIBAN” KOTA PEKALONGAN

Community activities in the commercial sector have an impact on the economic development of the city which is the influence of the city development into a town center trade. one of which is the existence of market tiban. A consequently of market activity tiban cause problems such as disrupting the flow of traffic as well as general merchandise facilities held alongside a road or along the corridor on the right or left, so that it is distrub traffic flow. The purpose of this study to find patterns in the market movement tiban Pekalongan. In this study, use a qualitative descriptive approach rationalistic. To achieve the goal of the analysis is then performed 3 analyzes the spatial pattern of movement does not include time tiban market movements, modes of movement, and activity systems analysis contained in tiban market, analysis of spatial movement patterns include cross- sectional analysis of the road corridor, stall dimensions, system settings tiban market, and movement patterns tiban market, and analyzes the factors behind the choice of location which is distance tiban market location, location tiban traders in the market, the similarity of merchandise, consumer offerings, the number of visitors, and mindset. Based on the analysis the pattern of market movements in Pekalongan tiban following reasons traders in making trading location and tiban market always occur on the same day and location. The recommendations contained in this study the need for regulation and supervision of the government in market activity in Pekalongan to minimize the problems effect of market activity tiban. There needs to be awareness of traders and buyers in the market tiban activities to keep abreast of the prevailing order in Pekalongan. There a good management of community market tiban to be organized and able to maximize the benefits of market tiban as evening entertainment for the citizens of Pekalongan.

Implanted Nanosensors in Marine Organisms for Physiological Biologging: Design, Feasibility, and Species Variability.

In recent decades, biologists have sought to tag animals with various sensors to study aspects of their behavior otherwise inaccessible from controlled laboratory experiments. Despite this, chemical information, both environmental and physiological, remains challenging to collect despite its tremendous potential to elucidate a wide range of animal behaviors. In this work, we explore the design, feasibility, and data collection constraints of implantable, near-infrared fluorescent nanosensors based on DNA-wrapped single-wall carbon nanotubes (SWNT) embedded within a biocompatible poly(ethylene glycol) diacrylate (PEGDA) hydrogel. These sensors are enabled by Corona Phase Molecular Recognition (CoPhMoRe) to provide selective chemical detection for marine organism biologging. Riboflavin, a key nutrient in oxidative phosphorylation, is utilized as a model analyte in in vitro and ex vivo tissue measurements. Nine species of bony fish, sharks, eels, and turtles were utilized on site at Oceanogràfic in Valencia, Spain to investigate sensor design parameters, including implantation depth, sensor imaging and detection limits, fluence, and stability, as well as acute and long-term biocompatibility. Hydrogels were implanted subcutaneously and imaged using a customized, field-portable Raspberry Pi camera system. Hydrogels could be detected up to depths of 7 mm in the skin and muscle tissue of deceased teleost fish ( Sparus aurata and Stenotomus chrysops) and a deceased catshark ( Galeus melastomus). The effects of tissue heterogeneity on hydrogel delivery and fluorescence visibility were explored, with darker tissues masking hydrogel fluorescence. Hydrogels were implanted into a living eastern river cooter ( Pseudemys concinna), a European eel ( Anguilla anguilla), and a second species of catshark ( Scyliorhinus stellaris). The animals displayed no observable changes in movement and feeding patterns. Imaging by high-resolution ultrasound indicated no changes in tissue structure in the eel and catshark. In the turtle, some tissue reaction was detected upon dissection and histopathology. Analysis of movement patterns in sarasa comet goldfish ( Carassius auratus) indicated that the hydrogel implants did not affect swimming patterns. Taken together, these results indicate that this implantable form factor is a promising technique for biologging using aquatic vertebrates with further development. Future work will tune the sensor detection range to the physiological range of riboflavin, develop strategies to normalize sensor signal to account for the optical heterogeneity of animal tissues, and design a flexible, wearable device incorporating optoelectronic components that will enable sensor measurements in moving animals. This work advances the application of nanosensors to organisms beyond the commonly used rodent and zebrafish models and is an important step toward the physiological biologging of aquatic organisms.

Complex biogeographical patterns support an ecological connectivity network of a large marine predator in the north‐east Atlantic

AbstractAimThe knowledge of a species biogeographical patterns greatly enhances our understanding of geographical ecology, which can improve identifying key conservation needs. Yet, this knowledge is still scarce for many marine top predators. Here, we aim to analyse movement patterns and spatial structuring of a large predator, the short‐finned pilot whale Globicephala macrorhynchus, over a wide geographical area.LocationNorth‐east Atlantic, in Macaronesian archipelagos (Azores, Madeira and Canaries) and Iberian Peninsula (Sagres).MethodsWe used likelihood techniques to estimate residency times and transition probabilities and carried out social analysis from individual photographic identification data, and analysed year‐round distribution from effort‐related sightings, collated between 1999 and 2015.ResultsThe best‐fitting models included emigration and reimmigration and showed different residency times within each archipelago. A total of 26 individual movements from 21 individuals (from a sample of >2,300 individuals) were recorded between Madeira and the neighbouring archipelagos, and heterogeneous transition probabilities were estimated within and between areas. A social network diagram showed associations from animals with distinct residency patterns. Higher significant sighting rates were recorded during autumn in the Azores and Madeira.Main conclusionsThe variation in site fidelity and year‐round occupancy among areas of the Macaronesia is consistent with some degree of population structuring, which combined with a connectivity network and seasonal inflows from animals inhabiting offshore waters, support the development of a complex social and geographical ecology in short‐finned pilot whales. The combination of techniques applied in this study was an effective way to estimate parameters of movement, which could be a good practice to be used for other scenarios and species.