Perspective matters : individual and species movement in spatial context

My presentation is part of my dissertation supported by Nordic Council and Lincoln Institute of Land Policy. As my research is not complete yet, there are a lot of questions without any answer. I try to concentrate my presentation to the divergence between valuation theory and practice in cases of transitional countries using mass appraisal. I have my own experience from Latvia (1999) and Estonia (1993; 1996; 2001) and it helps me to find some common lines for those countries, which can be similar for some other transitional economies. The property tax is an important fiscal device for countries in transition, and the successful application of mass appraisal is critical to its success. Valuation theory is part of it but it seems that administrative and legal issues are sometimes even more important. As my research is part of dissertation it can be useful to participate in Doctoral Session.

Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions

Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two‐dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an r package, which provides tools to animate movement trajectories, overlaying simultaneous uni‐ or multi‐temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame‐wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates.

The Global Positioning System (GPS) is a popular and widely used tool for finding location outdoors. However, it has some limitations, especially in areas where signals can bounce off buildings or objects, causing errors. This paper introduces a solution using sensor fusion—a technique that combines information from different sensors. In this case, it uses both GPS and motion data from a smartphone’s accelerometer. These two sources are combined using a Kalman filter, a smart algorithm that helps improve accuracy. As a result, the system provides more reliable and precise navigation. GPS error is determined by the horizontal accuracy. The horizontal accuracy of collected GPS data ranges from 21.5 meters to 3.94 meters, which varies according to the surroundings, obstacles, etc. After the fusion, the error is reduced to the range of 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> degrees for both latitude and longitude, equal to 1.11 meters. A chi-square test is a statistical technique used for comparing the actual result and the prediction. The test was conducted to ensure that 99.95% of the measured data points lie within a certain range. The boundary of a certain region is determined by the vertical and horizontal accuracy of the GPS.

The accuracy of global positioning system (GPS) locations obtained from study animals tagged with GPS monitoring devices has been a concern as to the degree it influences assessments of movement patterns, space use, and resource selection estimates. Many methods have been proposed for screening data to retain the most accurate positions for analysis, based on dilution of precision (DOP) measures, and whether the position is a two dimensional or three dimensional fix. Here we further explore the utility of these measures, by testing a Telonics GEN3 GPS collar's positional accuracy across a wide range of environmental conditions. We found the relationship between location error and fix dimension and DOP metrics extremely weak (r 2 adj ∼ 0.01) in our study area. Environmental factors such as topographic exposure, canopy cover, and vegetation height explained more of the variance (r 2 adj = 15.08%). Our field testing covered sites where sky‐view was so limited it affected GPS performance to the degree fix attempts failed frequently (fix success rates ranged 0.00–100.00% over 67 sites). Screening data using PDOP did not effectively reduce the location error in the remaining dataset. Removing two dimensional fixes reduced the mean location error by 10.95 meters, but also resulted in a 54.50% data reduction. Therefore screening data under the range of conditions sampled here would reduce information on animal movement with minor improvements in accuracy and potentially introduce bias towards more open terrain and vegetation.

Aim Models predicting the spatial distribution of animals are increasingly used in wildlife management and conservation planning. There is growing recognition that common methods of evaluating species distribution model (SDM) accuracy, as a global overall value of predictive ability, could be enhanced by spatially evaluating the model thereby identifying local areas of relative predictive strength and weakness. Current methods of spatial SDM model assessment focus on applying local measures of spatial autocorrelation to SDM residuals, which require quantitative model outputs. However, SDM outputs are often probabilistic (relative probability of species occurrence) or categorical (species present or absent). The goal of this paper was to develop a new method, using a conditional randomization technique, which can be applied to directly spatially evaluate probabilistic and categorical SDMs.Location Eastern slopes, Rocky Mountains, Alberta, Canada.Methods We used predictions from seasonal grizzly bear (Ursus arctos) resource selection functions (RSF) models to demonstrate our spatial evaluation technique. Local test statistics computed from bear telemetry locations were used to identify areas where bears were located more frequently than predicted. We evaluated the spatial pattern of model inaccuracies using a measure of spatial autocorrelation, local Moran’s I.Results We found the model to have non‐stationary patterns in accuracy, with clusters of inaccuracies located in central habitat areas. Model inaccuracies varied seasonally, with the summer model performing the best and the least error in areas with high RSF values. The landscape characteristics associated with model inaccuracies were examined, and possible factors contributing to RSF error were identified.Main conclusions The presented method complements existing spatial approaches to model error assessment as it can be used with probabilistic and categorical model output, which is typical for SDMs. We recommend that SDM accuracy assessments be done spatially and resulting accuracy maps included in model metadata.

Movement ecology is a rapidly emerging new research field that explores the underlying mechanisms of organism movement, from animals, humans, to plant and microorganisms, across a range of environmental contexts and spatiotemporal scales, from individual behavior to population-level processes. Research in this area draws on a diverse range of disciplines including animal behavior, ecology, biology, geography, physics, mathematics, statistics, and computer science, and utilizes a range of techniques and technologies, including telemetry, remote sensing, and bioacoustics. Animal movements are determined by a combination of both innate individual characteristics and environmental factors, such as food availability, predation risk, and habitat quality. Movements can range from small-scale foraging excursions within a home-range to seasonal migrations over thousands of kilometers, with characteristic patterns at different spatial scales. Animal movement may affect reproductive success, genetic diversity, and disease transmission within populations, and can play a role in wider ecosystem functioning via nutrient cycling or dispersal of seeds and pollen. The rapid growth of movement ecology since the early 2000s has arguably been driven by the increased functionality, availability, and durability of modern biologgers and sensors enabling detailed data sets to be collected from a wide range of species. In turn, this has necessitated the development of new mathematical, statistical, and computational methods for modeling and analyzing these rich, and increasingly very large, data sets. In its broadest sense, movement ecology encompasses a wide-ranging set of concepts, theories, and practical approaches that relate to animal movement. Rather than attempting to cover the entire field, this bibliography specifically focuses on recent developments in modeling and analysis of the most common type of animal movement data, namely (x,y) or (x,y,z) positional data, as collected directly or indirectly over time by spatial tracking systems such as GPS or radio telemetry. Data collected in other forms such as via mark-recapture studies is beyond our scope (although still an important approach within the field). The bibliography highlights the key Movement Ecology Textbooks in the field, explores key Movement Ecology Concepts, and explains methods for Movement Data Collection and Processing and dealing with Circular Statistics. The article gives an overview of Random Walk Models of Movement and how they relate to Movement Path Analysis and Metrics and Statistical Model Fitting of Movement Data in this context. It focuses on several key applications: Foraging and Search, Home-Range, Habitat Space-Use and Resource Selection, and Collective Movement, and finishes with a summary of recent R Packages for Movement Ecology.

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Animal tracking and biologging devices record large amounts of data on individual movement behaviors in natural environments. In these data, movement ecologists often view unexplained variation around the mean as “noise” when studying patterns at the population level. In the field of behavioral ecology, however, focus has shifted from population means to the biological underpinnings of variation around means. Specifically, behavioral ecologists use repeated measures of individual behavior to partition behavioral variability into intrinsic among-individual variation and reversible behavioral plasticity and to quantify: a) individual variation in behavioral types (i.e. different average behavioral expression), b) individual variation in behavioral plasticity (i.e. different responsiveness of individuals to environmental gradients), c) individual variation in behavioral predictability (i.e. different residual within-individual variability of behavior around the mean), and d) correlations among these components and correlations in suites of behaviors, called ‘behavioral syndromes’. We here suggest that partitioning behavioral variability in animal movements will further the integration of movement ecology with other fields of behavioral ecology. We provide a literature review illustrating that individual differences in movement behaviors are insightful for wildlife and conservation studies and give recommendations regarding the data required for addressing such questions. In the accompanying R tutorial we provide a guide to the statistical approaches quantifying the different aspects of among-individual variation. We use movement data from 35 African elephants and show that elephants differ in a) their average behavior for three common movement behaviors, b) the rate at which they adjusted movement over a temporal gradient, and c) their behavioral predictability (ranging from more to less predictable individuals). Finally, two of the three movement behaviors were correlated into a behavioral syndrome (d), with farther moving individuals having shorter mean residence times. Though not explicitly tested here, individual differences in movement and predictability can affect an individual’s risk to be hunted or poached and could therefore open new avenues for conservation biologists to assess population viability. We hope that this review, tutorial, and worked example will encourage movement ecologists to examine the biology of individual variation in animal movements hidden behind the population mean.

BackgroundIndividual movement is critical to organismal fitness and also influences broader population processes such as demographic stochasticity and gene flow. Climatic change and habitat fragmentation render the drivers of individual movement especially critical to understand. Rates of movement of free-ranging animals through the landscape are influenced both by intrinsic attributes of an organism (e.g., size, body condition, age), and by external forces (e.g., weather, predation risk). Statistical modelling can clarify the relative importance of those processes, because externally-imposed pressures should generate synchronous displacements among individuals within a population, whereas intrinsic factors should generate consistency through time within each individual. External and intrinsic factors may vary in importance at different time scales.ResultsIn this study we focused on daily displacement of an ambush-foraging snake from tropical Australia (the Northern Death Adder Acanthophis praelongus), based on a radiotelemetric study. We used a mixture of spectral representation and Bayesian inference to study synchrony in snake displacement by phase shift analysis. We further studied autocorrelation in fluctuations of displacement distances as “one over f noise”. Displacement distances were positively autocorrelated with all considered noise colour parameters estimated as >0. We show how the methodology can reveal time scales of particular interest for synchrony and found that for the analysed data, synchrony was only present at time scales above approximately three weeks.ConclusionWe conclude that the spectral representation combined with Bayesian inference is a promising approach for analysis of movement data. Applying the framework to telemetry data of A. praelongus, we were able to identify a cut-off time scale above which we found support for synchrony, thus revealing a time scale where global external drivers have a larger impact on the movement behaviour. Our results suggest that for the considered study period, movement at shorter time scales was primarily driven by factors at the individual level; daily fluctuations in weather conditions had little effect on snake movement.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0038-5) contains supplementary material, which is available to authorized users.

BackgroundAnimals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis.MethodsWe propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms.ResultsWe show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement.ConclusionsNot only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

Exploring single-line walking in immersive virtual reality

Movement data comes in various forms, including trajectory data and checkpoint data. While trajectories give detailed information about the movement of individual entities, checkpoint data in its simplest form does not give identities, just counts at checkpoints. However, checkpoint data is of increasing interest since it is readily available due to privacy reasons and as a by-product of other data collection. In this paper we propose to use the Earth Mover’s Distance as a versatile tool to reconstruct individual movements or flow based on checkpoint counts at different times. We analyze the modeling possibilities and provide experiments that validate model predictions, based on coarse-grained aggregations of data about actual movements of couriers in London, UK. While we cannot expect to reconstruct precise individual movements from highly granular checkpoint data, the evaluation does show that the approach can generate meaningful estimates of object movements.

This paper reports on our efforts to collect daily COVID-19-related symptoms for a large public university population, as well as study relationship between reported symptoms and individual movements. We developed a set of tools to collect and integrate individual-level data. COVID-19-related symptoms are collected using a self-reporting tool initially implemented in Qualtrics survey system and consequently moved to .NET framework. Individual movement data are collected using off-the-shelf tracking apps available for iPhone and Android phones. Data integration and analysis are done in PostgreSQL, Python, and R. As of September 2020, we collected about 184,000 daily symptom responses for 20,000 individuals, as well as over 15,000 days of GPS movement data for 175 individuals. The analysis of the data indicates that headache is the most frequently reported symptom, present almost always when any other symptoms are reported as indicated by derived association rules. It is followed by cough, sore throat, and aches. The study participants traveled on average 223.61 km every week with a large standard deviation of 254.53 and visited on average 5.77 ± 4.75 locations each week for at least 10 min. However, there is no evidence that reported symptoms or prior COVID-19 contact affects movements (p > 0.3 in most models). The evidence suggests that although some individuals limit their movements during pandemics, the overall study population do not change their movements as suggested by guidelines.

Movement abnormalities, including movement slowing and irregular muscle contraction, exist in individuals with psychotic-like experiences (PLEs) and serve as vulnerable factors of developing psychotic diseases in the psychosis continuum. To date scarce studies have developed early intervention programs tackling these initial impairments, which may be caused by basal ganglia alterations, in the early stage of the psychosis course. Rhythmic auditory stimulation (RAS) is a technique of neurological music therapy and has been proved effective in inducing faster movements in patients with psychotic diseases. This pilot study examined if RAS incorporated in functional movement training reduced severity of movement slowing and irregular muscle contraction in individuals with PLEs. Seventeen individuals with PLEs were randomly allocated to receiving RAS or receiving no RAS and underwent daily 40-min movement training (picking up beans) for three weeks. This study used motion analysis to measure movement performance at pretest and posttest. Eighteen age- and gender-matched individuals without PLEs were also recruited to provide data of intact movements. Results showed that RAS may reduce severity of movement slowing and irregular muscle contraction in individuals with PLEs. This pilot study is one of the pioneering studies validating effectiveness of early intervention programs tackling movement abnormalities, which are initial impairments in the psychosis continuum, in individuals with PLEs.