text:
A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity
Eldar Rakhimberdiev ORCID: orcid.org/0000-0001-6357-61871,2,3,
David W. Winkler1,
Eli Bridge4,
Nathaniel E. Seavy5,
Daniel Sheldon6,7,
Theunis Piersma2,8 &
Anatoly Saveliev9
Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a need for analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertainty about the position estimates.
We developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks for animals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) an uncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatially explicit behavioural masks.
The model is implemented in a specially developed open source R package FLightR. We used the particle filter (PF) algorithm to provide relatively fast model posterior computation. We illustrate our modelling approach with analysis of simulated data for stationary tags and of real tracks of both a tree swallow Tachycineta bicolor migrating along the east and a golden-crowned sparrow Zonotrichia atricapilla migrating along the west coast of North America.
We provide a model that increases accuracy in analyses of noisy data and movements of animals with complicated migration behaviour. It provides posterior distributions for the positions of animals, their behavioural states (e.g., migrating or sedentary), and distance and direction of movement.
Our approach allows biologists to estimate locations of animals with complex migratory behaviour based on raw light data. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit a fast-growing number of tracking studies with this technology.
The ability to track animal movements across long distances has revolutionized our understanding of animal ecology and has been helpful to conservation [1, 2]. Until recently, our ability to record this information was limited to larger animals that could carry satellite transmitters. However, recent technological advances have developed miniaturized devices that extend our ability to track much smaller animals, especially migratory songbirds [3]. Solar geolocation data loggers (or geolocators), are simple animal tracking devices that record ambient light levels for the purpose of estimating the latitude and longitude of an animal that wears the device. One of advantage of geolocators is that they typically can record data for a year or longer, i.e. cover an annual migration cycle. Despite an ongoing miniaturization of GPS and other satellite-linked tracking devices, geolocators remain useful because their low mass (currently ca. 0.35 g) broadens the range of species that may be tagged [3]. Furthermore, current GPS tracking devices for small birds are limited to a relatively small (e.g. 8–10) number of locations that can be recorded, thus it is difficult to use them to generate information on departure and arrival dates. In addition, the simplicity of geolocators design makes them inexpensive, which opens the prospect of affordable population-wide studies of migration and migratory connectivity.
While the theory of estimating latitude and longitude from the elevation of the sun is not new, the process of reconstructing animal movements by using light intensity levels recorded with geolocators presents several challenges. From the perspective of the hidden Markov modelling framework [4], sketched in Fig. 1, positioning by light level requires at least two parts: (1) an observational model and (2) a process model. In the case of animal positioning we refer to these parts as physical and movement models, respectively. The most common approach to solar geolocation [5] uses a simplified physical model. This model, which is referred to as the 'threshold method', requires the definition of each twilight event in a dataset as the time point corresponding to the moment when solar irradiance reaches some arbitrary, but constant, threshold level ([6, 7], see first column in Table 1). Latitude is then estimated by the duration of time between consequent pairs of twilights and the longitude by the time of solar noon or midnight. This threshold approach is still widely used, but it is plagued by many well-known problems such as biased estimates [8], unrealistic assumptions of constant shading, and a null assumption of no movement [9]. Aside from its general simplicity and accessibility, the advantage of the threshold method is that it needs only one data point per twilight period, which makes it well suited to data storage by tiny and simple tags that log a very narrow band of light intensities and have very limited data storage capacity.
Sketch of solar geolocation principles and our method of analysis. A solar geolocator (shown in black on bird's back) records light levels and times (example raw data in lower panel). When the animal moves, its position is unobserved (hidden), but it can be estimated by the pattern of light changes measured at twilight. We combine a physical (observation) model about how light levels change with position and time with some basic knowledge of the patterns of movement between twilights (movement model) along with all previous and subsequent positions in a hidden Markov model framework. Then, using the particle filter, we arrive at the most likely position and movement for each twilight
Table 1 Review of the differences of the existing methods together with rationale for the current contribution
Early attempts to improve upon the results of threshold analyses involved the application of a specially developed state-space models on locations estimated with the threshold method [10]. These models served as post hoc smoothers and generally improved the estimates, but they did not erase biases because of the original biases in the threshold-based location estimates [11]. In response to the problems of the threshold method stemming from atmospheric properties implicit in the observational model, Ekstrom ([7, 12]; column 2 in Table 1) developed the template fit method. Based on the physics of the atmosphere he derived the relationship between solar angle (angle above the horizon) and near-surface light intensity. Ekstrom [12] showed that the template fit model was robust to the effects of shading (see equations 4–7 in the current contribution). Despite the great potential of the template fit method, it was not used in the next generation of methods nor, to our knowledge, in any published analyses of empirical data. The next generation of methods employed specially developed state space models that not only had a better observational model, but also incorporated assumption of animal movement [13, 14]. These models provide more accurate and precise position estimates and a measure of their uncertainty (see columns 3 and 4 in Table 1).
All the early approaches to solar geolocation were developed for the tracking of marine animals. This specialty called for relatively simple movement models that assumed somewhat constant movement and also incorporated geographic masking under the assumption that marine animals could only occur in the ocean [14] and are not able to move over the land masses. For many terrestrial organisms (e.g., migratory birds) more complex models are needed that can account for prolonged sedentary behaviour interspersed with-long distance movements. Here we describe an approach that attempts to fill the needs associated primarily with tracking small birds by implementing the following features into a state space model: (1) accommodating the narrow band light level data that are recorded by many tags used on birds; (2) allowing for systematic changes in habitats throughout the annual cycle to make the observational model maximally independent from shading regimes; (3) defining behavioural state parameters that describe a bird as either sedentary or migrating; (4) modelling total distance travelled between twilights as opposed to modelling average velocity; (5) masking that allows movements (migratory state) over unsuitable habitats but not settling (sedentary state).
The new analytic framework (implemented in FLightR R package [15]) provides the flexibility to incorporate all of these characteristics. We demonstrate the approach by analysing both simulated and real tracks of small migratory birds.
The example data and a sketch of our approach of inferring tag location are shown at Fig. 1.
Although the method we outline here is applicable to light level data collected by a wide variety of geolocator tags, we focus our example on data collected by the widely used Mk geolocators developed by British Antarctic Survey (BAS, Cambridge, United Kingdom). Although, BAS has produced several models, they generally save a maximum light level over time intervals ranging from 2 to 10 min (Fig. 1). These tags are optimized for recording low light levels within a narrow band of light intensities around twilight, which results in <10 points collected during each twilight. Thus, a typical dataset will be dominated by minimal values (=0) in response to darkness and maximal values (=64) for measurements in daylight.
To demonstrate our method, we used both real data from BAS tags and simulations of BAS tag data. The real data consist of two tracks. The first is a from a tree swallow Tachycineta bicolor tagged at the breeding grounds in southern Canada and flying to wintering grounds in Cuba by the North American East Coast. The second track is from a golden-crowned sparrow Zonotrichia atricapilla tagged on its wintering grounds in California and flying to breed to Alaska via the West Coast. The first track features a 2-min and the second employs a ten-minute light logging interval. The simulated data are representative of stationary (not moving) tags, and error associated with weather-related and behavioural shading was derived from real data collected by a tag attached to the above-mentioned tree swallow while it was stationary at a known location.
All existing analytical approaches, including the one presented here, focus primarily on light-level transitions that occur around twilight, so these twilight periods have to be extracted from the data in an automated [13] or semi-automated [5, 16] way. Light-level data collected with geolocators on birds are noisy because birds frequently change their light environment by moving in and out of nest boxes, natural cavities, or dense vegetation, so a completely automated method of detection of twilight events produces many false positives. For this reason we consider a user-controlled, semi-automated process for identifying twilight events in a dataset to be most effective. We used an online interface called TAGS (Totally Awesome Geolocator Service; http://tags.animalmigration.org) to visually inspect data and generate a vector of twilight event periods, both morning and evening, for each day of the year.
Hidden Markov model
Hidden Markov models are currently the most widely used framework for estimating animal positions and behaviour through time [17, 18]. For solar geolocation the application of hidden Markov models is intuitive as animal movements result in unobserved positions that must be estimated probabilistically (Fig. 1). We developed a model with two parts: the hidden process model of animal movement and the observational model of light measurements from the tag. For easy reference we will refer to these models taken together as the FLightR model.
Physical observation model
The observational model matches recorded light levels to theoretical expectations at different locations on Earth. During each twilight period i the tag records several measurements j. Estimation of the theoretical expectation consists of three steps. The first step transforms the coordinates of potential twilight location α k at time τ ij to angle of sun relative to horizon (solar angle, θ ij ) with standard astronomical equations [19].
$$ {\theta}_{ij}=l\left({\boldsymbol{\alpha}}_k,\ {\tau}_{ij}\right) $$
This step is shared by previous analytical approaches – the Trackit [13] and TripEstimation models [14, 16] and the current contribution.
At the next step, the expected light measurements (ELM) are calculated from solar angle. This step varies significantly across analytical approaches, all of which try to account for natural variation in the relationship between solar angle and measured light levels. Sources of this variation are scattering (which includes cloud cover, shading of the tag by landforms and plant leaves; see [9, 20]) and natural variation in refraction [7].
Nielsen and Siebert [13] assumed the function f relating solar angle to light measurements was unknown and estimated it using cubic splines with autocorrelation of spline parameters \( \varphi \sim \) between consecutive twilights
$$ EL{M}_{ij}={f}_{\varphi \sim}\left({\theta}_{ij}\right)+{\varepsilon}_{ij};\kern0.75em {\varepsilon}_{ij}\ \in\ Norm\left(0,\ {\sigma}_i\right) $$
The potential problem of this approach is that errors affecting light intensity are multiplicative, not additive [12]. This means that if light intensity errors are significantly larger than zero the result will be biased. Another drawback is that many light level measurements in each twilight period are required to fit the splines well, and for the widely used BAS geolocators, transitions from the maximum to minimum light-level values usually occur in less than five time steps.
Sumner et al. [14] accounted for the multiplicative error by taking the natural log of ELM and fitting the following equation:
$$ logEL{M}_{ij}=f\hbox{'}\varphi \sim \left({\theta}_{ij}\right)+{K}_i+{\varepsilon}_{ij};\kern0.75em {\varepsilon}_{ij}\ \in\ Norm\kern0.5em \left(0,\ {\sigma}_i\right) $$
The function f ' is nonlinear and is estimated for each tag with penalized splines on a calibration dataset. The variable K i is attenuation (or cloudiness) and is also estimated. This approach is less dependent on the shading bias but still requires many light level measurements during each twilight event.
Ekstrom [12] derived the following deterministic relationship (i.e. 'template') between light at surface (solar irradiance at Earth surface, SL) and solar angle θ.
$$ SL=\left\{\begin{array}{c}\hfill \frac{Cloudiness \times {e}^{-{u}^2}}{1+\mathrm{e}\mathrm{r}\mathrm{f}\left(-u\right)},\kern0.75em u\le 0\hfill \\ {}\hfill \frac{Cloudiness \times {e}^{-{u}^2}}{\mathrm{erfc}(u)},\kern0.75em u>0\hfill \end{array}\right.;\ u=21.5\times \sin \left(\theta \right) $$
where erf is the error function and erfc is the complementary error function. This equation can be rewritten as follows:
$$ \log (SL)= \log (Cloudiness)-\mathrm{u}{\left(\theta \right)}^2- \log \left(\mathrm{erfc}\left(u\left(\theta \right)\right)\right);\kern0.5em u\left(\theta \right)=21.5\times \sin \left(\theta \right) $$
Note that log (Cloudiness) plays the same role as the parameter K in equation 3.
In the FLightR observational model, we define
$$ {f}^{\hbox{'}\hbox{'}}\left(\theta \right)=-\mathrm{u}{\left(\theta \right)}^2- \log \left(\mathrm{erfc}\left(u\left(\theta \right)\right)\right) $$
$$ \log \left(S{L}_{ij}\right)={f}^{\hbox{'}\hbox{'}}\left({\theta}_{ij}\right)+{K}_i+{\varepsilon}_{ij};\kern0.75em {\varepsilon}_{ij}\ \in\ Norm\left(0,\ {\sigma}_i\right) $$
The function f '' is known, in contrast to the unknown function f ' in equation 3.
Equations 4–6 deal with the relationship between the solar angle and light reaching surface of Earth but not to the light measured by a tag. To do that, we need to account for the properties of the tag. Assuming linearity (on a log scale) of the tag measurements we define
$$ \log \left( EL{M}_{ij}\right)=I+Z\times \log \left(S{L}_{ij}\right) $$
Where tag intercept I and tag slope Z are tag specific and do not depend on position or time.
The complete equations for the ELM are then:
$$ \log \left( EL{M}_{ij}\right)=Z\times {f}^{\hbox{'}\hbox{'}}\left({\theta}_{ij}\right)+I+K{\hbox{'}}_i + \varepsilon {\hbox{'}}_{ij};\kern0.5em \varepsilon {\hbox{'}}_{ij}\ \in\ Norm\left(0,\ {\sigma}_i\right) $$
Here, the attenuation and noise terms as K' j and ε' ij apply directly to log (ELM ij ) are not the same as the attenuation and noise terms in eqn. 6. The observed light measurement (OLM) depends on the attenuation K' i and slope Z that both vary from day to day. The dependency of K' i on the animal's behaviour and habitat led us to focus only on Z for estimating the likelihood that the observed light measurements were produced at location α k. We assumed the observations come from a model with tag slope Z i that is a random variable independent of the animals' behaviour. This allows us to estimate the distribution of Z i from the calibration dataset. In all the analysed calibration datasets Z i had a lognormal distribution:
$$ {Z}_i\ \in\ logNorm\left({Z}_{calib},\ \sigma {Z}_{calib}\right) $$
Parameters Z calib and \( {\sigma}_{Z_{calib}} \) are assumed to be constant and are estimated from the calibration at the known true sites, where model 8 was fitted.
The complete equations for the observed light measurement are then
$$ \log \left(OL{M}_{ij}\right)={Z}_i\times {f}^{\hbox{'}\hbox{'}}\left(\theta ij\right)+I+K{\hbox{'}}_i+\varepsilon {\hbox{'}}_{ij};\kern0.5em {\varepsilon}_{ij}\ \in\ Norm\left(0,\ {\sigma}_{err}\right) $$
In fact, for given location α k and twilight i, eqn.10 is standard linear model of the form
$$ \log \left(OL{M}_{ij}\right)={a}_i+{Z}_i\times {b}_{ij}+{c}_j;\kern0.5em {c}_j\ \in\ Norm\left(0,\ {\sigma}_c\right) $$
with known b ij , unknown a i , unknown Z i , and random error c j assumed to be normally distributed with a mean of zero. Thus, for any location α k we can apply a standard least squares procedure to estimate the mean Ẑ i and standard deviation \( {\sigma}_{Z_i} \) of the unknown Z i and then use the following probability density as a surrogate for the hypothesis that the data were observed at location α k:
$$ {Z}_i\ \in\ Norm\left({Z}_i\widehat,\ {\sigma}_{Z_i}\right) $$
Thus we can integrate product of density from eqn. 9 and eqn. 12 over all Z and estimate the required probability density and the likelihood of data at α k.
Parameters Z calib and σ calib must be estimated for every tag from twilights recorded at a known position. This means that the tag has to be calibrated in a known position for at least 5 days before the animal leaves the area or after it arrives back. The calibration does not need to be a clear-sky calibration and preferably should be done on the bird. The casing around the light sensor may discolour as the tag ages, such that the calibration generated when the tag is deployed will not match the calibration when the tag is recovered. If known locations are available for the beginning and end of the tag use, it is possible to account for ageing by assuming a linear change in the calibrated slope throughout the deployment period.
Movement model
Previous movement models for solar geolocation were developed for marine animals that are assumed to travel at a relatively constant velocity. For birds, we know that there are long periods (up to 6 months) when birds are primarily sedentary (moving less than several km a day), punctuated by short periods (1–2 weeks) when birds migrate rapidly between breeding and non-breeding locations, but during these periods may spend several days at a single site to refuel before migrating again [21]. For these highly variable movement patterns we needed a more flexible movement model. We used a simplified "Double" model [22, 23] with just two behavioural states: "Sedentary" and "Migrating". In the context of these two states, we defined the position at the twilight α i+1 as:
$$ {\boldsymbol{\alpha}}_0 = \mathrm{initial}\ \left(\mathrm{released}\ \mathrm{location}\right),\ \mathrm{vector}\ \mathrm{of}\kern0.5em \mathrm{coordinates}\left(\mathrm{x};\ \mathrm{y}\right) $$
$$ {\boldsymbol{\alpha}}_{i+1}=\left\{\begin{array}{c}{\boldsymbol{\alpha}}_i+{d}_i,\kern0.95em with\ probability\ {p}_i\\ {}\hfill \kern3em {\alpha}_i,\kern0.95em with\ probability\ 1-{p}_i\hfill \end{array}\right. $$
Where an offset d i follows an uncorrelated random walk with a distribution reflecting bird behaviour and described by a set of parameters:
$$ {d}_i=\left[\begin{array}{c}\hfill {S}_i\times \cos \left({\Phi}_i\right)\hfill \\ {}\hfill {S}_i\times \sin \left({\Phi}_i\right)\hfill \end{array}\right] $$
At each moment i, d i is distributed as a mixture of a zero increment (no movement) with probability p i , and non-zero increment in direction Φ i and step length S i . We assigned distributions Φ i and S i as follows:
$$ {\varPhi}_i \sim \mathrm{vonMises}\left(\varphi,\ k\right) $$
$$ {S}_i \sim \mathrm{truncNorm}\left(\upmu,\ \sigma,\ a,\ b\right) $$
For all runs in our models, we assumed that the truncation points were a = 45 and b = 1000 km. That is, when birds initiated a movement, they could not fly less than 45 km or more than 1500 km in a single between-twilight interval. Parameters φ and k in the von Mises distribution reflect direction of migration and its concentration, and the parameters of the truncated normal distribution for distance shape the distribution of inter-twilight flight distances. R packages 'circular' [24] and 'CircStats' [25] were used to obtain random draws from von Mises distributions given the parameters and 'truncnorm' [26] for the truncated normal distribution. All movement was modelled as being uncorrelated in time, assuming that distance flown, direction and behavioural switches during bird migration may be highly independent one day to the next.
Finally, to allow for migration across water bodies, we introduced a spatial behavioural mask that prevents birds from entering a sedentary state in locations corresponding with large water bodies. Hence, birds may fly over water, but cannot switch to sedentary mode in this habitat.
Many techniques may be used to estimate a hidden Markov model. Our uncorrelated random walk model has five unobserved variables at each of many time steps. Bayesian methods are particularly useful for computing posterior distribution over these variables. The Kalman filter [27] or unscented Kalman filter [28] are other approaches that can be applied to the problem of inference in a long state space model [13], but these do not apply to highly nonlinear posterior distribution patterns caused by non-Gaussian light error distribution with spatially explicit masks. To accommodate these aspects of geolocator data analysis we decided to (1) discretize space and (2) use the particle filter [29] for approximating the posterior distribution. Spatial discretization with creation of a regular grid accelerates computational workflow by minimizing amount of possible between node transitions and also puts no constrains on the spatial error distribution [18, 30, 31]. We used the function regularCoordinates() from the geosphere R package [32] to discretize a state-space into a regular grid with a distance of 50 km between nodes. Choice of the distance of 50 km between grid nodes was arbitrary, and followed the idea that it should be small enough to have posterior probabilities distributed at several grid cells at any time, but at the same time not too small as it will make grid larger and estimation slower. All the movements were estimated between these nodes and the observational model was estimated at these nodes. The approach allows us to "obtain a numerical non-parametric representation of the probability distribution of the animal's position. This probability distribution illustrates the uncertainty of the estimated movement with a high degree of detail. Finally, we can draw inference about parameters in a likelihood framework, compute the most probable track of the animal, and sample random tracks that the animal may have traveled" [31].
Following Patterson et al. [17], we used a particle filter (PF) or 'sequential importance resampling'. Our implementation of the PF was based on the algorithm from Doucet et al. [33] as expanded by Andersen et al. [34]. The logic of the PF is as follows. At the initialisation phase, our PF creates a sample of 106 particles (points with positions) at the actual release point. Then, for each particle, it generates a new position at i + 1 from the process model. All new particle locations are then resampled proportionally to their weight (product of the previous likelihoods and the current likelihood, estimated by the observational model and a priori constraints as explained below). The resampled set of particles proceeds to the next step and the process repeats. In other words, PF creates 106 possible paths that develop from the release point according to the rules for the movement model. At every twilight all 106 particles are compared to data passed through the observation model. After each check all unlikely particles are replaced by likely ones with the probability of their relative likelihood. Once all iterations are completed, the histories of the remaining particles render a distribution that approximates the spatial probability distribution for the bird at each twilight.
Because of the degeneracy problem common for long PF runs (see e.g. [35]), we used block sampling [36] in our PF. assuming that at n steps before current step i the particles have reached their global optimum. We selected n = 90 and then estimated the current weight of the particle as the mean of the logarithms of particle weights at steps [i-n]:n.
To include information on where the bird was recaptured when the geolocator was removed, we used the approach described by Andersen et al. [34]. Here the last n states of particle histories were resampled with weights proportional to their probability density function from a normal distribution with a mean of the recapture point coordinates and standard deviation (SD) equal to some measure of precision (because we had 50 km between grid points, we used a SD of 25 km).
A priori constraints in the model can be defined according to any biological assumptions. These constraints are used at the resampling stage as weights, and they are not optimised. Here, we present the model with two constraints: a general spatial mask and a behavioural spatial mask. The general spatial mask works through exclusion of selected grid nodes and is binary. Excluding land, for example, may be useful for modelling fish, marine mammal, or pelagic bird movements. The behavioural mask is described above and lowers probability for animals to adopt a stationary state in particular nodes. Value of zero for this mask will completely prevent sedentary mode at the node.
The PF offers a relatively fast optimization technique, but it is unstable to outliers [37]. Outliers in twilight data can happen if an animal stays inside a cavity or nest box during twilight and emerges later, resulting in what would appear to be a late and atypically fast twilight. Ideally such points should be removed during visual inspection of the twilights, but in order to make the PF stable to occasional undetected outliers we have developed two outlier detection approaches. The first approach is used after tag calibration but before the particle filter run. The main idea of the approach is to estimate likely longitude of crossing with the equator for each twilight and then use time series outlier detection software to identify outliers [38, 39]. The other approach is an 'on the go' outlier detection technique. This filtering step is implemented at twilight i when new particle positions were generated but before the resampling for the next step occurs. At this point, the average distance from i − 1 → i is compared with i → i + 1, and if the latter is smaller and mean turning angle at i is less the 100°, then twilight i is considered to be outlier and its likelihood surface is not used in the particle resampling.
Example 1: simulated stationary tags
We have simulated year-long light data for stationary tags using eqns 9 and 10. The following parameters for the simulations were estimated from real data collected by a tag attached to a Tree Swallow while it had been in a known location for ten days, so they contain a realistic distribution of shading and of slopes.
$$ I+K+Z\kern0.5em \in\ Norm\left(6.14,\ 1.01\right);\ {Z}_{calib} = 0.23;\ {\sigma}_{Z_{calib}}=0.01;\ {\sigma}_{err}=0.32 $$
We began by estimating locations with the threshold method implemented in GeoLight. For this analysis, we used the month of July as the calibration period. Then, using the same calibration period, we estimated the locations in FLightR. We did not use any behaviour or spatial masks and confined our analysis to a reasonably large area with radius of 1000 km around the tag location for the potential spatial extent required for the FLightR runs. For all the simulation and real tag runs we used the same priors of 0.1 for probability of migratory behaviour and 300 ± 150 km for distance covered between twilights. We consider these settings to be suitable for most of the animals except ones that move longer distances between twilights. In our experience with different tags, distance priors do not affect results of the model run, if they are generally correct and wide enough. The probability of migration prior does have an effect, and if selected too high, the resulting track can become noisy. A value of 0.1 was selected as a prior on the basis of good results with simulated data and real tracks; we do not recommend changing it without a specific reason.
To compare the performances of GeoLight and FLightR, we estimated monthly biases (known minus estimated location) and SD associated with each analysis type for both latitude and longitude (Table 2). Monthly estimates of latitude were not biased when estimated by FLightR (bias ranged from −0.1° to 0.03° due to some rounding error from estimation on a grid) whereas GeoLight biases in latitude estimation ranged from −2.41° to 3.63°. Moreover the FLightR errors on latitude are almost uniform across the year, with SD ranging from 0.23° to 0.33°, whereas the Geolight SD of monthly errors ranged from 1.02° to 10.96° (Fig. 2). Note that the GeoLight method does not provide estimates for latitude at the time around equinox without assumption of stationarity. The results in Table 2 show that estimates from the threshold method are biased while FLightR produces unbiased and consistent estimates.
Table 2 Average bias and SD estimated by GeoLight and FLightR for simulated stationary tags at 5° N and 55° N. GeoLight calibration was done by July twilights
Estimated position of simulated stationary tag by classic approach (GeoLight, points) and FLightR (median estimated latitude with 50 and 95 % credible intervals)
Example 2: tree swallow - 2 min fixes
To demonstrate the application of the FLightR functions to a real-world geolocator dataset, we analysed the track from a single tree swallow that was fitted with an Mk12S geolocation logger on 23 June 2011 at the Long Point Bird Observatory in Ontario, Canada (80.46° W 42.62° N). It was recaptured at the same location and the tag was removed on 7 June 2012. Geolocator was developed by the British Antarctic Survey (Cambridge, United Kingdom) and had 15-mm-long stalk positioned at a 30° angle. The tag recorded maximum light levels at 2 min intervals.
This single track is a small part of a much larger study with many individuals tracked with geolocators at over ten sites across North America (Bradley et al. in prep.). We provide raw geolocator data and code to estimate positions for both of the packages online at https://github.com/eldarrak/FLightR/blob/master/examples/tree_swallow_BAS_tag_example/tree_swallow_analysis.Rmd. Note that during the GeoLight run no filters or outlier detection tools were used, and FLightR detected as outliers and excluded data from ~30 twilights. We present four different ways of looking at the path to better reconstruct its details and implications. The reconstructed path from FLightR (coloured symbols in Fig. 3) has the bird staying in the region of Long Point through early July. The bird appears to have stayed at the same longitude and latitude (Fig. 4), and this is backed up with the highest confidence in the reconstructed path (Fig. 5). On the morning of 13 July, the bird departed Long Point (a diurnal departure indicated by the orange dot in Fig. 6a) and made a single flight of over 500 km (Fig. 6b, line segment A in Fig. 3) to a stopover site in Virginia (yellow dots in Fig. 3). It stayed in the same general area until 1 or 2 August, when it departed (segment B Fig. 3) and flew over 200 km to a site near the coastal border of North Carolina and Virginia (Fig. 3). This movement is less certain in its particulars, having interquartile ranges of 1–2 degrees (Fig. 5). The interquartile ranges of position estimates for the following weeks are near the equinox (and thus very unreliable for latitude measures) and vary from low (<1 degree) to very high (>5 degrees; Fig. 5). The bird departed the region on 23 or 24 October, heading south and inland (segment C), and very likely not stopping until reaching coastal South Carolina, where it remained until about 2 November. It then departed for Cuba (segment D), spending most of the rest of the winter there. The one exception to this Cuba winter residency may have been a departure (segment E) to the vicinity of the Bimini Islands on or about 28 February, returning to Cuba about 5 March, but uncertainties at this time of year, nearing the equinox, are very high (Fig. 5). Much more certain is that the winter residency ended on 28 March, when the bird flew from Florida to the coast of North Carolina/Virginia (segment F). It did not stay there for long, if at all, and continued migrating until it reached Long Point on 6 April. The interpretation of the locations during this last northward leg are complicated by the large latitudinal uncertainties associated with the vernal equinox (Fig. 5). The customary approach using thresholds with GeoLight generally agreed with the FLightR estimates.
The track of a tree swallow as estimated from 2-min fixing interval data by classic approach (GeoLight, grey line and dots) and FLightR. Inset shows Tree Swallow range in North America. The medians of twilight positions estimated by FLightR are coloured by the month of a year (colours for each month are illustrated with pie chart). In June a bird was tagged on the breeding grounds at the Long Point Bird Observatory in Ontario, Canada. July 19 it left breeding ground and moved to the stopover site in Virginia (segment A). At 1or 2 August it moved to coastal area on the border of North Carolina and Virginia (segment B), where remained stationary until end of October. 23–24 October the bird departed towards a stopover site in South Carolina (segment C), where it stayed for a week and then continued south to Cuba (segment D). The bird remained in Cuba till the end of March, with one exception for flight to the vicinity of the Bimini Islands (segment E). On 28 of March bird left wintering grounds and migrated to the North Carolina/Virginia site (segment F) and after a short stopover there moved straight to the breeding grounds (segment G). Note that no spatial or behavioural masks were used for the FLightR run, so positions were allowed to be everywhere. Raw GeoLight estimates shown on the figure should not be interpreted as a positions and inference can be made only for most likely location during stationary periods, new functions in GeoLight 2.0.1 are available now for the estimation of these most likely stationary locations (Lisovski & Liechti, pers. comm). Tree swallow range image is courtesy of Birds of North America: Cornell Lab of Ornithology
Longitudes (upper panel) and latitudes (lower panel) of a track of a tree swallow as estimated from 2-min fixing interval data by classic approach (GeoLight, dots) and FLightR. The medians of twilight positions estimated by FLightR are shown with accompanying quartile ranges and 95 % credible intervals. Note absence of the latitudinal positions from GeoLight during the equinoxes (shown by red vertical lines)
Precision of the estimates of positions by longitude (orange) and latitude (blue) of a track of a tree swallow shown by the interquartile ranges
Medians of probability of migration (upper panel) and migration distance with corresponding quartiles (lower panel) for a tree swallow estimated by FLightR. Nocturnal migration is shown in grey and diurnal migration in orange. The circles show transitions which were characterized by shift of the median latitude and/or median longitude. Vertical red lines mark equinoxes
Example 3: golden-crowned sparrow - 10 min fixes
Our sample dataset for golden-crowned sparrow was collected from a bird that was tagged on its wintering grounds in coastal California and tracked to the breeding grounds on the coast of Alaska [40]. The bird was captured and tagged on 2 February 2010 at the Palomarin Field Station in coastal California, United States (37.93° N, 122.74° W). It was recaptured at the same location and the tag was removed on 19 October 2010. The tag was an Mk10S geolocators developed by the British Antarctic Survey (Cambridge, United Kingdom) with a 15-mm-long stalk positioned at a 30° angle. The tag recorded maximum light levels at 10 min intervals. Note that during the GeoLight runs no filters or outliers detection tools were used, and FLightR detected as outliers and excluded about 40 twilights out of 600.
The reconstructed track for the golden-crowned sparrow indicates a movement from the wintering grounds in California, north along the west coast of North America to the breeding grounds in Alaska (Fig. 7). After it was tagged in early February, the bird was sedentary until 18 April, and clear migration activity was initiated May 13 and ceased in early June (Fig. 8). This is consistent with post-tagging observations of the bird that confirmed that it was present (and likely sedentary) at the tagging sight until at least March 31. During the migration both latitude and longitude have high uncertainty (Fig. 9), during the breeding season at Alaska uncertainty in longitude remains high. During spring migration, daily movements were primarily between 500 and 700 km, with some as long as 800 km (Fig. 10). Fall migration occurred between 13 September and 14 October (Fig. 10). During both spring and fall migrations, movements were both diurnal and nocturnal.
The track of a golden-crowned sparrow as estimated from 10-min fixing interval data by the classic approach (GeoLight, grey line and dots) and by FLightR. Inset shows golden-crowned sparrow range in North America. The medians of twilight positions estimated by FLightR are coloured by the month of a year (colours for each month are illustrated with pie chart). Bird was tagged in 2 February on the wintering grounds in California. It remained close to the capture site till 13 May and then started northward migration. After two weeks of migration it arrived on the breeding grounds (~1 June), and remained there until at least 5 July. It may have moved about 200 km westward after breeding, though the uncertainty here is high. 10 September bird started migration to the wintering grounds, where it arrived at in the end of October. Note that no spatial or behavioural masks were used for the FLightR run, so positions were allowed to be everywhere. Raw GeoLight estimates shown on the figure should not be interpreted as a positions and inference can be made only for most likely location during stationary periods, new functions in GeoLight 2.0.1 are available now for the estimation of these most likely stationary locations (Lisovski & Liechti, pers. comm). Golden-crowned sparrow range image is courtesy of Birds of North America: Cornell Lab of Ornithology
Longitudes (upper panel) and latitudes (lower panel) of a track of a golden-crowned sparrow as estimated from 10-min fixing interval data by classic approach (GeoLight, dots) and FLightR. The medians of twilight positions estimated by FLightR are shown with accompanying quartile ranges and 95 % credible intervals. Note absence of the latitudinal positions from GeoLight during the equinoxes (shown by red vertical lines)
Precision of the estimates of positions by longitude (orange) and latitude (blue) of a track of golden-crowned sparrow shown by the interquartile ranges
Medians of probability of migration (upper panel) and migration distance with corresponding quartiles (lower panel) for a golden-crowned sparrow) estimated by FLightR. Nocturnal migration is shown in grey and diurnal migration in orange. The circles show transitions which were characterized by shift of the median latitude and/or median longitude. Vertical red lines mark equinoxes
Uncertainty in both longitude and latitude increased during both migrations. Notably, uncertainty in longitude also increased around summer solstice in the end of June (Fig. 9). This may be because both twilight lines become almost horizontal and parallel to each other in summer at high latitudes, thus limiting inference on the longitudinal position. The extreme case of this happens at the Arctic Circle (66° N) at solstice. This situation reflects one of the two major limitations in solar geolocation – (1) determination of latitude near equinoxes at any longitude and (2) determination of longitude near solstice at high latitudes. The FLightR model was able to cope with these limitations and provided meaningful positioning results.
Current model performance
We have presented a new model for the analysis of geolocator data. By analyzing both simulated data and real tracks, we demonstrated that this approach has greater precision than the threshold method (Table 2), which is by far the most commonly applied analysis technique in the literature [41]. The positions obtained for a simulated stationary tag did not have a systematic bias and were reasonably good even near the equinox. For two birds that migrated along the coasts of North America, our approach, without any geographic masks, reconstructed a path that closely matched the coastline. In addition to providing more precise locations, this method provides data on the probability of movement behaviour that can be used to test behavioural hypotheses.
Our method offers several improvements over existing methods for estimating locations from light-level data. To our knowledge, this is the first non-proprietary application that extends template-fitting method all the way from a physical/astronomical model to reconstructing tag locations using timed light-level data. Our method works well on tags that record a relatively narrow band of light intensity data, something that has been problematic with other alternatives to the threshold method [16]. Furthermore, this method applies a movement model that replaces the assumption of constant movement throughout the annual life cycle with a model that allows staying or moving at variable distances at each daily step in the annual path. However, there are still limits to location estimation from light-level data. For actively moving real birds, the estimated latitudes near equinoxes are very imprecise, as there is virtually no latitudinal information in the data at that time. Conclusions about the latitudes of locations during or near the equinoxes must still be made with great caution.
Optimization with current implementation of the particle filter is not fast. One run of the FLightR model takes about 1 h on a four core laptop, which is slower than the Kalman filter approach used in trackit (less than 1 h, [13]) but faster than MCMC in tripEstimation (about 4 h, [16]). The main benefit of the particle filter though is that it not only saves the posterior distribution of particles at each step but also a posterior distribution of all transitions. All of these products are valuable for assessing the confidence and reliability of each of the inferred locations and shifts in behaviour produced by FLightR.
Drawbacks and directions for further development
The present version of FLightR is very general and could be improved in several ways. The movement part could be improved by introduction of scale-free continuous power-law distance distributions and discrete power-law durations of stopover distributions [42]. The observational model would also be very much improved if we could derive a single general closed-form likelihood equation. The particle filter is not the fastest optimization method, and some other approaches should eventually be tried, such as unscented Kalman filter [28] and the Forward-Backward algorithm [43]. We hope that all these and other potential developments improvements will be facilitated by the current publication and in a few years will bring the field of solar geolocation path reconstruction from its infancy to adulthood.
Another potential area of improvement would be to work with hardware developers to increase the quality of data stored on geolocator tags. Many current tags have variable, and often unmeasured, problems in their lack of calibration, clock drift and change in tag opacity and colour throughout the year. Clock drift is likely to affect the estimated longitudes, while changes in opacity are still not very well understood. Simple calibration procedures and studies of aging effects could help alleviate these problems.
It is perhaps surprising that most geolocators measure only a few points at twilight, which is the most crucial measurement period. The introduction of 'smart' recording modes that would allow intense sampling at twilight while skipping uninformative midnight and noon measurements would dramatically improve the precision of location estimation.
A reflection on the general 'infancy' of a burgeoning research field
Solar geolocation publications on migratory birds are booming now in the peer reviewed journals, with rather little attention to the statistical methods necessary to improve the objectivity and precision of path reconstructions. Although some conclusions may be made warranted without statistical approaches (e.g. longitude of breeding or wintering grounds), we argue that path-reconstruction of moving animals from noisy light and time data should be done within a statistical framework. Statistical models do not provide exact positions of the bird, and biological interpretations should always be cognizant of estimates of the position and/or timing uncertainty provided by FLightR or any other analysis package. For example, interpretation of the estimated time spent inside a key habitat is much more appropriate than estimating a single fix within that habitat [14].
Here we introduced a template fitting observational model for solar geolocation as a part of a hidden Markov model. With this approach we estimated positions and migratory schedules of animals accompanied by precision estimates. We believe that the present approach will establish a new benchmark for geolocator analysis that meets the standards applied to most other subfields of research with regard to analytical vigor, objectivity and repeatability.
Availability of supporting data
Tree Swallow geolocator data, FLightR package and the example of the analysis can be found at GitHub page of the package: https://github.com/eldarrak/FLightR.
Wikelski M, Kays RW, Kasdin NJ, Thorup K, Smith JA, Swenson GW. Going wild: what a global small-animal tracking system could do for experimental biologists. J Exp Biol. 2007;210:181–6.
Bowlin MS, Bisson I-A, Shamoun-Baranes J, Reichard JD, Sapir N, Marra PP, et al. Grand challenges in migration biology. Integr Comp Biol. 2010;50:261–79.
Bridge ES, Thorup K, Bowlin MS, Chilson PB, Diehl RH, Fléron RW, et al. Technology on the move: recent and forthcoming innovations for tracking migratory birds. Bioscience. 2011;61:689–98.
Baum LE, Petrie T. Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Stat. 1966;37:1554–63.
Lisovski S, Hahn S, Hodgson D. GeoLight - processing and analysing light-based geolocator data in R. Methods Ecol Evol. 2012;3:1055–9.
Hill C, Braun MJ. Geolocation by light level – the next step: latitude. In: Sibert JR, Nielsen J, editors. Electronic tagging and tracking in marine fisheries. The Netherlands: Kluwer Academic Publishers; 2001. p. 315–30.
Ekstrom PA. An advance in geolocation by light. Mem Natl Insitute Polar Res Spec Issue. 2004;58:210–26.
Musyl MK, Brill RW, Curran DS, Gunn JS, Hartog JR, Hill RD et al. Ability of archival tags to provide estimates of geographical position based on light intensity. In Electronic Tagging and Tracking in Marine Fisheries. Dordrecht: Springer; 2001:343–67.
Lisovski S, Hewson CM, Klaassen RHG, Korner-Nievergelt F, Kristensen MW, Hahn S. Geolocation by light: accuracy and precision affected by environmental factors. Methods Ecol Evol. 2012;3:603–12.
Sibert JR, Musyl MK, Brill RW. Horizontal movements of bigeye tuna (Thunnus obesus) near Hawaii determined by Kalman filter analysis of archival tagging data. Fish Oceanogr. 2003;12:141–51.
Sibert JR, Nielsen A, Musyl MK, Leroy B, Evans K. Removing bias in latitude estimated from solar irradiance time series. In: Nielsen JL, Arrizabalaga H, Fragoso N, Hobday A, Lutcavage M, Sibert J, editors. Tagging and tracking of marine animals with electronic devices. Volume 9. Dordrecht: Springer Netherlands; 2009. p. 311–22.
Ekstrom P. Error measures for template-fit geolocation based on light. Deep Sea Res Part II Top Stud Oceanogr. 2007;54:392–403.
Nielsen A, Sibert JR. State-space model for light-based tracking of marine animals. Can J Fish Aquat Sci. 2007;64:1055–68.
Sumner MD, Wotherspoon SJ, Hindell MA. Bayesian estimation of animal novement from archival and satellite tags. PLoS ONE. 2009;4, e7324.
R Core Team. R: A Language and Environment for Statistical Computing. 2015th ed. Vienna, Austria: R Foundation for Statistical Computing; 2015.
Sumner M, Wotherspoon S. tripEstimation: metropolis sampler and supporting functions for estimating animal movement from archival tags and satellite fixes. 2011.
Patterson T, Thomas L, Wilcox C, Ovaskainen O, Matthiopoulos J. State–space models of individual animal movement. Trends Ecol Evol. 2008;23:87–94.
Pedersen MW, Patterson TA, Thygesen UH, Madsen H. Estimating animal behavior and residency from movement data. Oikos. 2011;120:1281–90.
Meeus J. Astronomical Algorithms. 2nd ed. Willmann-Bell: Richmond; 1998.
Fudickar AM, Wikelski M, Partecke J. Tracking migratory songbirds: accuracy of light-level loggers (geolocators) in forest habitats. Methods Ecol Evol. 2012;3:47–52.
Newton I. The Migration Ecology of Birds. London: Elsevier-Academic Press; 2008.
Morales JM, Haydon DT, Frair J, Holsinger KE, Fryxell JM. Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology. 2004;85:2436–45.
McClintock BT, King R, Thomas L, Matthiopoulos J, McConnell BJ, Morales JM. A general discrete-time modeling framework for animal movement using multistate random walks. Ecol Monogr. 2012;82:335–49.
Agostinelli C, Lund U. Circular: Circular Statistics. 2011.
Lund U, Agostinelli C. CircStats: Circular Statistics. 2009.
Trautmann H, Steuer D, Mersmann O, Bornkamp B. Truncnorm: Truncated Normal Distribution. 2011.
Kalman RE. A new approach to linear filtering and prediction problems. J Basic Eng. 1960;82:35.
Julier SJ, Uhlmann JK. In: Orlando KI, editor. Proc. SPIE. Volume 3068. 1997. p. 182–93.
Kitagawa G. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J Comput Graph Stat. 1996;5:1–25.
Thygesen UH, Pedersen MW, Madsen H. Geolocating fish using hidden Markov models and data storage tags. In: Nielsen J, Arrizabalaga H, Fragoso N, Hobday A, Lutcavage M, Sibert J, editors. Tagging and tracking of marine animals with electronic devices. Volume 9. Netherlands: Springer; 2009. p. 277–93.
Royer F, Lutcavage M. Positioning pelagic fish from sunrise and sunset times: complex observation errors call for constrained, robust modeling. In: Nielsen J, Arrizabalaga H, Fragoso N, Hobday A, Lutcavage M, Sibert J, editors. Tagging and tracking of marine animals with electronic devices. Volume 9. Netherlands: Springer; 2009. p. 323–41.
Hijmans RJ, Williams E, Vennes C. Geosphere: Spherical Trigonometry. 2012.
Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering. Stat Comput. 2000;10:197–208.
Andersen KH, Nielsen A, Thygesen UH, Hinrichsen HH, Neuenfeldt S. Using the particle filter to geolocate Atlantic cod (Gadus morhua) in the Baltic Sea, with special emphasis on determining uncertainty. Can J Fish Aquat Sci. 2007;64:618–27.
van der Merwe R, Doucet A, de Freitas N, Wan E. The unscented particle filter. Technical Report CUED/F-INFENG/TR 380, Cambridge University Engineering Department.
Doucet A, Briers M, Sénécal S. Efficient block sampling strategies for sequential Monte Carlo methods. J Comput Graph Stat. 2006;15:693–711.
Arulampalam MS, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process. 2002;50:174–88.
López-de-Lacalle J. Tsoutliers: Detection of Outliers in Time Series. 2015.
Chen C, Liu L-M. Joint estimation of model parameters and outlier effects in time series. J Am Stat Assoc. 1993;88:284–97.
Seavy NE, Humple DL, Cormier RL, Gardali T. Establishing the breeding provenance of a temperate-wintering North American passerine, the golden-crowned sparrow, using light-level geolocation. PLoS ONE. 2012;7, e34886.
Bridge ES, Kelly JF, Contina A, Gabrielson RM, MacCurdy RB, Winkler DW. Advances in tracking small migratory birds: a technical review of light-level geolocation: Light-Level Geolocation Dataloggers. J Field Ornithol. 2013;84:121–37.
Clauset A, Shalizi CR, Newman MEJ. Power-law distributions in empirical data. SIAM Rev. 2009;51:661–703.
Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE. 1989;77:257–86.
Hill RD. Theory of geolocation by light levels. In. Elephant seals: Population ecology, behavior, and physiology. Edited by La Boeuf BJ, Laws RM. Dordrecht: University of California Press; 1994:227–36.
We thank Ryan Norris and David Bradley for kind permission of use their unpublished data on the tagged tree swallow and to Jeffrey Kelly and Andrea Contina, Simeon Lisovski and an anonymous reviewer or their comments on a draft. ER and TP were supported by grants to TP from Waddenfonds (Metawad, WF209925) and the Netherlands Organisation for Scientific Research (NWO, TOP-grant ALW-TOP/Shorebirds in space/11.024 to TP); ESB was supported by NSF EAGER 0946685 and IDBR-1152356, and ER and DWW were supported by NSF grants DBI-0963969 and IDR-1014891. We also acknowledge cyberinfrastructure provided by NSF: 0919466 "Collaborative Research: EPSCoR RII Track 2 Oklahoma and Kansas: cyberCommons", J. Duckles for his help with Linux environment and the cyberinfrastructure provided to Cornell University by the NSF Expeditions in Computing grant on Computational Sustainability (Award Number 0832782) and Computing research infrastructure for constraint optimization, machine learning, and dynamical models for Computational Sustainability grant (Award Number 1059284). We thank all the participants of NCEAS Project 12663 'Establishing an open-source animal-tracking analysis platform for archival geolocators'. Finally, collaboration among the authors was facilitated by the MIGRATE research coordination network (NSF IOS-541740).
Department of Ecology and Evolutionary Biology and Laboratory of Ornithology, Cornell University, Ithaca, 14853, USA
Eldar Rakhimberdiev
& David W. Winkler
Department of Marine Ecology, NIOZ Royal Netherlands Institute for Sea Research, PO Box 59, 1790, AB Den Burg, The Netherlands
& Theunis Piersma
Department of Vertebrate Zoology, Biological Faculty, Lomonosov Moscow State University, Moscow, 119991, Russia
Oklahoma Biological Survey, University of Oklahoma, 111 E Chesapeake St., Norman, OK, 73019, USA
Eli Bridge
Point Blue Conservation Science, 3820 Cypress Drive, Suite 11, Petaluma, CA, 94954, USA
Nathaniel E. Seavy
College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, 01003, USA
Daniel Sheldon
Department of Computer Science, Mount Holyoke College, South Hadley, MA, 01075, USA
Chair in Global Flyway Ecology, Conservation Ecology Group, Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, PO Box 11103, Groningen, 9700 CC, The Netherlands
Department of Ecological Systems Modelling, Institute of Environmental Sciences, Kazan Federal University, 5 Tovarisheskaya, Kazan, 420008, Russia
Anatoly Saveliev
Search for Eldar Rakhimberdiev in:
Search for David W. Winkler in:
Search for Eli Bridge in:
Search for Nathaniel E. Seavy in:
Search for Daniel Sheldon in:
Search for Anatoly Saveliev in:
Correspondence to Eldar Rakhimberdiev.
ER and DW specified the problem and verbally developed the first approaches to its solution, ER and AS developed the detailed method. ER coded the package and drafted the manuscript. DS and AS formalized and corrected the equations. NS, EB, DW and TP helped to draft the manuscript. All authors read and approved the final manuscript.
Rakhimberdiev, E., Winkler, D.W., Bridge, E. et al. A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity. Mov Ecol 3, 25 (2015). https://doi.org/10.1186/s40462-015-0062-5
FLightR
Solar geolocation
Template fitting


text:
Amitabh Behar
CEO of Oxfam India
About Amitabh Behar
Amitabh Behar is the CEO of Oxfam India. As Chief Executive officer, he spearheads the organizations vision to fight against inequality, poverty and injustice in the country, besides carrying forward its humanitarian work.
He has over 22 years of experience as a civil society leader. Amitabh is a former Executive Director of National Foundation for India. He got recognition for his work on governance accountability, social and economic equality, and citizen participation.
He is one of the leading experts of people-centric advocacy and chairs organizational boards of Amnesty International India, Navsarjan, and Yuva. He is the vice board chair of CIVICUS and also sits on the board of other Organisations like Centre for Budget and Governance Accountability (CBGA), Mobile Crèche, VANI, Global Fund for Community Foundation (GFCF).
Amitabh holds an M. Phil in Political Science from Jawahar Lal Nehru University. He was also a research fellow at the University Grants Commission.
| {{ copy }} {{ /rawImageName }}

redpajama_set_name: RedPajamaCommonCrawl


The Uniqsis FlowLab™ range are affordable and easy-to-use flow chemistry systems ideal for use in research, education and for those wishing to try flow chemistry without the associated costs of a fully automatic system. Built around the same high quality components as used in the widely acclaimed Uniqsis FlowSyn™ system, both the FlowLab™ and FlowLab Cold™ offer uncompromised performance at an entry-level price.
The FlowLab™ system comprises two high pressure pumps, a HotCoil™ coil reactor station and FlowLab™ system control computer. The computer, pumps and HotCoil™ are connected over a LAN using an Ethernet hub.


text:
Downtown Rome set to gain STEAM
Project Fibonacci Foundation eyes new collaborative high-technology center in community
ROME Project Fibonacci Foundation, a non-profit charitable organization registered in New York, has plans to bring a fully operational STEM plus arts (STEAM) center to downtown Rome by early 2023. …
Drozd
Posted Wednesday, February 9, 2022 6:30 am
ROME - Project Fibonacci Foundation, a non-profit charitable organization registered in New York, has plans to bring a fully operational STEM plus arts (STEAM) center to downtown Rome by early 2023.
The facility is envisioned to be a collaborative center focused on programs designed to grow the local youth workforce in STEM (science, technology, engineering and mathematics). Aptly named the Multiversity of Science, Art, and Technology — or MOSART — the center will be part interactive exhibition center featuring artificial intelligence (AI) inspired art including traditional art works, part local venue for special community activities and guest speaker events, and part metro-hub for local STEAM leadership education programs.
According to Foundation Chairman Dr. Andrew Drozd, MOSART will be a community asset operated by the foundation.
The foundation's board is raising funds for the project and seeks community support. Through public grant programs, tax-deductible private donations, and corporate donations the foundation anticipates raising $1 million this year. The funds would cover facility acquisition, renovations, first-year operating expenses and job creation.
The foundation will offer a limited, multi-tiered first-founders club for organizations, families, individuals, and other stakeholders who wish to make a major, tax-deductible donation towards the project on a first-come, first-served basis. The foundation will dedicate office rooms, building wings, and founders' bricks in the name of donors, family members, or organizations to establish a durable community legacy.
Included will be an array of permanent exhibits that pay tribute in name and spirit to the leaders of industry, technology and manufacturing who built the Mohawk Valley focusing on aspects of science, technology, arts, and history.
It will showcase the influencers who contributed to our economy, history, and cultural enrichment.
"This is a chance for community members and stakeholders to step up and positively influence our future and build on the shoulders of its citizen youth for generations to come," said Drozd.
The multiversity branding, according to Drozd, reflects a combination of ideas: the cosmological 'big bang' and infinite multiverses, university education and diversity of ideas within the context of multidisciplinary learning.
"This is an entirely new value proposition and a futuristic museum concept," said Drozd. "MOSART will be a popular destination for schools and visiting organizations. Downtown Rome, and its Downtown Revitalization Initiative renaissance, is a logical choice for its physical presence with its proximity to a premier research laboratory and emerging art scene. MOSART will neither be only a museum nor only a learning-activities center, but both and more, and it will deliver on the goals of the Upstate Revitalization Initiative (URI) strategy."
The URI strategy includes expanding STEM industries and cultivating the STEM workforce through innovation hub and entrepreneurial network growth.
"A dedicated MOSART center establishes permanent roots and deepens our commitment to the local community in support of job growth," Drozd added.
Drozd anticipates 4-6 new jobs being created initially. He also anticipates hosting outside groups with common interests in promoting STEAM leadership education for STEM workforce development.
Drozd said he believes the Mohawk Valley community benefits from an energetic, forward thinking youth culture centered on science, art and technology looking ahead to consider what future generations will inherit.
"Rather than an image of a retirement village, we must expand our youth workforce and shore up the local youth talent pool by guiding and developing career opportunities," said Drozd. "But more work is needed to connect students to area job opportunities to keep them local."
MOSART will highlight our area's strengths in science and technology, the expanding Arts District, its rich history and cultural diversity and especially its people, businesses and industries as a way of capturing youth.
"What if we could capture lightning in a bottle by spotlighting our area's contributions in science, art and technology all in one place and present it in a compelling way?," asked Drozd. "Consider the Mohawk Valley's rich legacy in engineering, technology and manufacturing and the movers and shakers who made it happen."
Drozd suggested hosting joint-community special events at MOSART in cooperation with the Capitol Theatre, Rome Art and Community Center, Munson Williams Proctor Arts Institute in Utica, Rome Historical Society, Fort Stanwix National Monument, Griffiss Institute, SUNY Polytechnic, Mohawk Valley Community College and other organizations. The long-term collaboration with these and other community partners will ensure their success and sustainability in the community, Drozd said.
Activity rooms or maker spaces would be used to host the foundation's annual array of STEAM programs from sculpture spaces to joint-research apprenticeships, mini-drone camps and the annual STEAM conference workshops.
Exhibits will span the history of the Mohawk Valley's technological achievements. SciArt exhibits and projects would include resonance art, sound sculpting, bioinspired engineering, the science of art conservation, and the art and math of the cosmos. Displays could include pendulum clocks, walking on water exhibits, art through Chemistry, and more.
AI-inspired art is another offering where the public would be able to interact with technoart exhibits powered by AI for a unique discovery-learning experience. The foundation has already reached out to the Smithsonian Museum in Washington, D.C. and World Science Festival in New York City to explore partnerships for traveling science-art exhibits in the future.
Asked why art plays such an important role, Drozd said, "Ours is not a STEM program. It is no surprise though that art can positively affect STEM outcomes, but we have examined that impact more deeply."
Drozd and his team have conducted in-depth research on human and machine learning frameworks noting that World War II codebreakers in the Pacific Theatre were largely musicians and mathematicians. Drozd is working on the STEM-Prism project which uses AI as a delivery platform for STEM plus arts education and hopes to monetize it as a product to generate sustainable revenues for MOSART.
"The broader vision of STEAM is to Stimulate Thought-leadership, Experience, and Advancement of the Mind through entrepreneurial, community, and economic well-being," said Drozd. "In addition to an exhibition floor, offices, activity and maker spaces, we would look to lease out space to other organizations for us to generate steady recurring revenues."
The foundation is in discussions with multiple property owners to determine the best option for MOSART.
The board, which includes business-side Executive Director Bob Bojanek and educator-side Executive Co-director Tamalin Martin, both of Rome, along with a group of celebrity/industry advisors and a representative from NASA's Johnson Space Center, is committed to realizing the project to its fruition.
ANDRO Computational Solutions, LLC has announced it will contribute up to $50,000 to kick-start a corporate matching program on behalf of MOSART.
For more information on fund-raising options and to participate in an opportunity to create a new legacy for future growth and prosperity of Rome and the greater Mohawk Valley, contact Dr. Drozd at adrozd@projectfibonacci.org or call 315-335-1238.


Digital Camera Photographer of the Year is back for 2019 with an amazing £30,000 prize fund up for grabs. This year the awards were free to enter thanks to the generous support of our sponsors, mpb.com and Affinity Photo.
Winners will also be featured in Digital Camera magazine and sister publications, plus exposure through our social media. Not only that, get your work exhibited at The Photography Show and an invitation to the special gala reception.
This year the awards are completely free to enter, and photographers 25 years and younger can take part in both the main awards AND Young Photographer of the Year.
For the overall winner the judges will be looking for a standout photographer who has not only won a round (or several) but has also demonstrated a range of skills and visual awareness in different categories.


G46-B6/2/386 Gallery Wall Sconces MURANO VENETIAN STYLE ALL-CRYSTAL WALL SCONCE WITH CRYSTAL BALLS!
G46-B6/2/386 MURANO VENETIAN STYLE ALL-CRYSTAL WALL SCONCE WITH CRYSTAL BALLS!
THIS MAGNIFICENT WALL SCONCE IS ALL 100% CRYSTAL. Nothing is quite as elegant as the fine crystal wall sconces that gave sparkle to brilliant evenings at palaces and manor houses across Europe. This beautiful wall sconces is decorated with 100% crystal that capture and reflect the light of the candle bulbs, each resting in a scalloped bobache. The crystal glass arms of this wonderful wall sconces give it a look of timeless elegance that is sure to lend a special atmosphere in any home. This wall scones is dressed with spectacular crystal balls which take the sparkle to an entirely new level of brilliance!


text:
Law Office of John L. Calcagni, III
John L. Calcagni III
John L. Calcagni III - Providence, RI
72 Clifford St #300 Providence, RI 02903
Your Profile? Update Now
Providence Criminal Litigation Lawyer
Attorney Calcagni is skilled at both criminal and civil litigation. He formerly served as a military prosecutor in the U.S. Army JAG Corps and Special Assistant U.S. Attorney with the U.S. Department of Justice. Military Defense Attorney Calcagni is admitted to practice in the United States Court of Appeals for the Armed Forces and the U.S. Army Court of Criminal Appeals, as well as the state and federal courts in Rhode Island, Massachusetts, New York, Connecticut, and Florida...
Attorney Calcagni is skilled at both criminal and civil litigation. He formerly served as a military prosecutor in the U.S. Army JAG Corps and Special Assistant U.S. Attorney with the U.S. Department of Justice. Military Defense Attorney Calcagni is admitted to practice in the United States Court of Appeals for the Armed Forces and the U.S. Army Court of Criminal Appeals, as well as the state and federal courts in Rhode Island, Massachusetts, New York, Connecticut, and Florida.
Attorney Calcagni prosecuted hundreds of crimes involving drug use, possession and distribution; simple and domestic assault; assault with deadly weapons; gang-related assault; manslaughter; murder; attempted murder; possession, manufacture and distribution of child pornography; solicitation of minors; child abuse; rape; carnal knowledge or statutory rape; weapons-related charges; burglary; breaking and entering; possession of bomb-making materials; terrorism-related offenses; fraud; identity fraud; larceny; destruction of property; financial crimes; traffic violations; driving while under the influence of alcohol or drugs; trespassing; conspiracy; and attempt-related offenses.
He now uses the extensive investigation and trial experience he acquired as federal prosecutor to represent his clients in Criminal Defense matters in State, Federal and Military Courts.
Attorney Calcagni is also an experienced civil litigator. He formerly belonged to a boutique civil litigation firm that specializes in representing Plaintiffs in Complex Personal Injury actions such as medical malpractice, motor vehicle accidents, nursing home negligence, and product liability.
Attorney Calcagni represented Plaintiffs in all aspects of civil litigation, both in discovery and at trial. His clients are well-served by his pretrial investigation, trial preparation and commitment to maximizing the value of each case for settlement or trial.
Law Firm Info
FindLaw.com is doing its part in combating fake reviews. All reviews:
Go through email verification, and reviewers must verify their email addresses.
Can be confirmed and flagged as a verified client by law firms.
Can be reported and subjected to removal if they violate FindLaw.com's Terms of Service.
Click here to view our Terms of Service.
About John L. Calcagni III
Drug Use, Possession and Distribution
Simple and Domestic Assault
Assault With Deadly Weapons
Gang-Related Assault
Manufacture and Distribution of Child Pornography
Solicitation of Minors
Carnal Knowledge or Statutory Rape
Weapons-Related Charges
Breaking and Entering
Possession of Bomb-Making Materials
Terrorism-Related Offenses
Destruction of Property
Driving While Under the Influence of Alcohol or Drugs
Attempt-Related Offenses
Rhode Island, 2003
Massachusetts, 2003
Connecticut, 2005
Florida, 2008
U.S. District Court Northern District of New York, 2004
U.S. District Court Eastern District of New York, 2010
U.S. District Court Southern District of New York, 2010
U.S. District Court District of Rhode Island, 2008
U.S. District Court District of Massachusetts, 2008
U.S. District Court District of Connecticut, 2009
U.S. District Court for the District of Columbia, 2010
U.S. Court of Appeals 1st Circuit, 2004
U.S. Court of Appeals 2nd Circuit, 2011
U.S. Army Court of Criminal Appeals, 2011
U.S. Court of Appeals for the Armed Forces, 2011
The Judge Advocate General's School U.S. Army, Charlottesville, Virginia, 2004
Roger Williams University School of Law, Bristol, Rhode Island, 2003
Honors: magna cum laude
Bryant University, Smithfield, Rhode Island, 2000
Honors: cum laude
U.S. Army Reserves – Judge Advocate General's Corps, 2008-Present
U.S. Army Trial Defense Service, Senior Defense Counsel
U.S. Army Trial Defense Service, Deputy Regional Defense Council
U.S. Army – Judge Advocate General's Corps., 2004-2008
Office of the Staff Judge Advocate
10th Mountain Division (Light Infantry), Fort Drum, New York
Chief, Military Justice, 2006-2007
Special Assistant U.S. Attorney, 2004-2008
Other Sources of Feedback About John L. Calcagni III
Included on the 2022 Rhode Island list
Past Lists
2021 Rhode Island Super Lawyers list
View the profile for John L. Calcagni III on Super Lawyers
Offers Free Initial Consultation
http://www.calcagnilaw.com/ (Rhode Island Criminal Defense)
When viewing a listing, consider the state advertising restrictions to which lawyers and law firms must adhere, as well as our FindLaw.com Legal Directory disclaimer. Some lawyers publish comparative information regarding the services that they provide which may be subject to specific comparative communications restrictions.
1. Rating
How would you rate your overall experience with this lawyer?
Use the up and down arrows to increase or decrease the rating
Please Rate
Optional ratings
Professional Competence
Clear Ratings
Please share your opinion of working with this attorney. Do not reveal personal information or details about your case. All submitted reviews are shared with the attorney prior to publishing.
Your feedback must be at least 50 characters.Your review must be at least 50 characters
I would recommend this attorney to friends and family: Yes
First name First Name provided is not valid
Last name Last Name provided is not valid
For verification purposes only.
E-mail provided is not valid
Keep my name anonymous from public view: Yes
Hired Attorney Consulted Attorney Please choose one
Service start year Service Start Year is not valid
Service end year Service End Year is not valid
By submitting this form you agree to receive email communications from FindLaw related to the management of your review.
Privacy Statement Contact Lawyer FAQ
Reason for reporting this post Please select an option
Email (for verification purposes only) E-mail provided is not valid
* Email your@email.com
* Tell us about your legal need
Phone xxx-xxx-xxxx
DISCLAIMER: The information contained in this web site is intended to convey general information. It should not be construed as legal advice or opinion. It is not an offer to represent you, nor is it intended to create an attorney-client relationship. The use of the internet or this contact form for communication is not necessarily a secure environment. Contacting a lawyer or law firm email through this service will not create an attorney-client relationship, and information will not necessarily be treated as privileged or confidential.
www.calcagnilaw.com/


text:
compare and contrast essay
From the era of 1400s till 1800s, music was at peak and has brought impacts in people's life. More musical instruments were invented and musicians were well known at that time with the purpose to entertain them. By then, musical instruments started to spread widely around the world. Musical instrument is defined as any form of potential devices that gives musical sound. I will contrast two musical instruments, banjo and guitar in terms of their history, their characteristics, and the styles to play them. Yet, people will still know that they share the common similarities in terms of categorized as the strings-family instrument, the portability and how they are played. First, according to Ruehl (2014) in the aspect of history, the banjo begun from Africa and the African slaves brought into the America in the 19th century. The slaves were not allowed to play drums. So, they invented the banjo. The Americans concluded it as a bad thing until the Civil War ended. Then, a performer of the "blackface" of the Americans, John Sweeney was credited for the fifth string he added onto the banjo. He was known for his attribution in introducing the banjo in England and US during his tour. Despite that, many people could not believe that he was the one that added the fifth string as a picture of a slave holding a five-string banjo was painted before his birth. The Americans participated in the picking competitions in urban places and at the same time the evolution of banjo finger-picking arouse at the end of 19th century. In special occasions, the banjo used to perform in ballrooms along with singing and dancing (Tythacott, 2001). Ruehl (2014) also pointed out some well known players like Eddie Peabody and Perry Bechtel. Today, it is still pop... ... middle of paper ... ....net/object/difference-between-a-guitar-and-a-ukulele H: (2010). The Road to the Stage: Road Ready Cases. History of the Guitar. Retrieved from http://www.roadreadycases.com/history-of-the-guitar.html P: Estrella, E. (2014). About.com: Music education. Profile of the Banjo. Retrieved from http://musiced.about.com/od/strings/p/banjo.htm Estrella, E. (2014). About.com: Music education. Profile of the Guitar. Retrieved from http://musiced.about.com/od/musicinstruments/p/guitar.htm T: Tythacott, L. , Shelton, A, (2001). Traditions around the world: Musical instruments. United Kingdom: Hodder Wayland. Cross, D. (2014). About.com: Guitar. Types of Guitar. Retrieved from http://guitar.about.com/od/typesofguitar/ss/types-of-guitar-banjo-nylon-12-string.htm W: (2014). Strings and beyond.com: What Are Guitar Strings? Retrieved from http://www.stringsandbeyond.com/what.html
The History of the Guitar
Works Cited Dickinson, Chrissie. "Inductees." Country Music Hall of Fame and Museum. N.p., n.d. 25 Feb. 2014 Kentor, Audry, and Steve Michael. "The History of the Guitar."
Faster than Fast
Web. 24 Jan. 2014. "Invention of the Electric Guitar :: Smithsonian Lemelson Center." Invention of the Electric Guitar :: Smithsonian Lemelson Center. N.p., n.d.
Why The Banjo Makes America Great
This just may change your mind. It was invented in the early 1800's by African slaves imitating instruments from their own country. The instrument most likely imitated was the banjar which was commonly made from a hollowed out gourd. Because of its origins, the banjo was an instrument untouched by white society until, according to drhorsehair.com, "A young man named Joel Walker Sweeney, of Appomattox Court House, VA, learned to play a four- string gourd banjo at age 13, from the black men working on his father's farm." When he was older, Joel added a shorter fifth string to the banjo, and started to tour.
Rhythm and Blues- R&B
Works Cited Burke, S. (2007, January 1). Rhythm of Blues. . Retrieved January 1, 2014, from http://rhythmofblues.blogspot.com/2007/11/rhythm-of-blues-history.html. Cahoon, B.
The Physics of a Guitar
26 May 2014. . "The Physics of the Acoustic Guitar - Body." The Physics of the Acoustic Guitar - Body. N.p., n.d. Web.
Music Gives Me the Blues
10 Works Cited
Carter, the patriarch of the legendary Carter Family, the first family of country music, met a blues guitarist by the name of Lesley "Esley" Riddle. Lesley Riddle had created a unique picking and sliding technique on the guitar while he was recovering from an accident on the job. The Carter Family was looking for a new sound of music, and they were so overwhelmed by the sound that Lesley produced, they wanted him to teach them how to play that way. Lesley Riddle influenced Maybelle Carter's style of guitar playing called the "Carter Scratch," which became legendary. According to birthplaceofcountrymusic.org, Riddle's influe... ... middle of paper ... ...ey cannot deny that the Blues has influenced different genres from the cotton fields to the Rock-and-Roll Hall of Fame.
The North Indian Percussion Instruments
"Tabla Gharanas". India Heritage. Web. 20 April 2014. http://www.indiaheritage.org/perform/music/h-i-ta-g.htm Wegner, Gert-Matthas. Vintage Tabla Repertory: Drum Compositions of North Indian Classical Music.
The Impact of the Electric Guitar on Music
Guitar Player, 32, 106-123. Retrieved January 4, 2002, from Proquest Database. Waksman, S. (n.d.). Instruments of desire: The electric guitar and the shaping of musical experience. Cambridge: Harvard.
Great Guitarist
Through out history, music has played a big role. It has let people communicate and others release their creative minds. Throughout the life of music, instruments have made it happen. The guitar has become one of the most popular of all instruments. In fact, almost every band heard on the radio has a lead guitar accompanying the vocals.
Physics of an Acoustic Guitar
10 Marshall Brain, "How Acoustic Guitars Work," Howstuffworks, March 18, 2003, <http://people.howtuffworks.com/guitar.html> (1998-2003). 11 Joe Wolfe, "How does a guitar work?" Research in Guitar Acoustics, March 24, 2003, <http://newt.phys.unsw.edu.au/music/guitar/intro_engl.html> (1997-2002).


text:
TDH reports no new deaths, 116 new COVID-19 cases in NETN on Monday
US speeds cases of translators, others blocked by travel ban
by: GENE JOHNSON, Associated Press
FILE – In this May 15, 2017 file photo, protesters wave signs and chant during a demonstration against President Donald Trump's revised travel ban outside a federal courthouse in Seattle. The Trump administration has agreed to speed up the cases of some former interpreters for the U.S. military in Iraq. The decision also affects hundreds of other refugees whose efforts to move to the United States have been in limbo since he announced his travel bans three years ago. (AP Photo/Ted S. Warren, File)
SEATTLE (AP) — President Donald Trump's administration has agreed to speed up the cases of some former interpreters for the U.S. military in Iraq and hundreds of other refugees whose efforts to move to the United States have been in limbo since he announced his travel bans three years ago.
The news was contained in a settlement filed in federal court in Seattle on Monday. It concerned more than 300 refugees who were on the verge of being permitted to come to America in 2017 when their applications were halted as part of Trump's efforts to restrict travel from several mostly Muslim nations.
Some of those affected are close relatives of refugees who are already in the U.S., while others are from 11 countries, including Egypt, Iran and Somalia, that Trump singled out, citing security reasons.
"The government tried to keep refugee families apart under the pretense of national security," said Lisa Nowlin, an attorney with the American Civil Liberties Union of Washington, which sued along with several other organizations. "This settlement aims to undo the harmful effects of the illegal and misguided ban on refugees."
The restrictions on refugees from the 11 countries and on relatives of those already in the U.S. — known as "follow-to-join" refugees — were companion measures to Trump's broader travel bans on those seeking visas to enter the U.S., which the Supreme Court eventually allowed.
U.S. District Judge James Robart in Seattle — the same judge who blocked Trump's initial, broader travel ban in early 2017 — blocked the companion refugee restrictions late that year in consolidated lawsuits that were brought by the ACLU, Jewish Family Services, International Refugee Assistance Project and other organizations. They alleged that the refugee bans were discriminatory and arbitrary and that they violated due process rights.
By the time Robart agreed to block the bans, hundreds of refugees had their cases upended, leaving them in administrative limbo. For many, background checks, medical clearances or other required documentation had expired by the time the bans were revoked. That meant they had to begin the process over again.
The plaintiffs included former interpreters for the U.S. military in Iraq, who sued under pseudonyms because they could face threats if their identities became public. Others were refugees who had petitioned to have their spouses and children join them in the U.S. from camps in Kenya, Uganda and elsewhere.
One plaintiff, Allen Vaught, a war veteran from Dallas, said the refugee ban "derailed efforts to get my last surviving Iraqi translator, who served bravely alongside U.S. military forces for many years, to the United States."
Under the settlement, the refugees won't automatically be admitted to the U.S., but the government agreed to move their cases to the front of the line for processing.
"What the administration did really messed up their cases," said Mariko Hirose, litigation director of the New York-based International Refugee Assistance Project. "This settlement is aimed at making sure that people who were affected by the ban are able to get their cases adjudicated and hopefully come to the U.S. very quickly."
The 11 countries were Egypt, Iran, Iraq, Libya, Mali, North Korea, Somalia, Sudan, the Republic of South Sudan, Syria and Yemen.
Regional MLK Jr. Day events hosted online amid pandemic
Effortless Girl: Using cane matting to make unique crafts to help keep us organized
250+ dogs rescued from 'bone yard' on Grundy County, Tenn. property
Clouds Early/Clearing Late Precip: 0%


Looking for customized CADCAM software for your company? Produsoft has experience in your sector.
Designer: the new CAD for CAM!
Produsoft is the CAD/CAM specialist for milling, turning, turn-milling, wire EDM, 2D and 3D sheet metal, engineering and DNC.
High amounts of simple products or unique pieces of the most complex products, Produsoft has the production software that meets your requirements!
Next to these international standard products, we have our own development department that allows us to deliver a fully customized solution meeting all of your specific needs.
Do you want to always stay informed whith our latest news?


WEST HARTFORD -- West Hartford authorities say three local children are expected to recover after being exposed to high levels of carbon monoxide in their home.
Firefighters responded to the home on Forest Hills Drive shortly before 4 a.m. Monday on a report of a strong smell coming from the basement.
The children are being treated at Connecticut Children's Medical Center. Their names haven't been released.
Fire officials say carbon monoxide was leaking from a malfunctioning oil-fired burner in the basement. They say there was some smoke and high levels of the gas in the home.


Remote controlled pan tilt base rotates and tilts. Pan tilt base operates via wired controller and/or wireless remote control base. Top mounting plate enables operator to attach a variety of lights, cameras and other equipment. Heavy duty remote controlled base operates on 12 or 24 volts.


The SCRN Exam Prep Package contains three of AANN's best-selling products: SCRN Review Course, SCRN Self-Assessment Exam, and the AANN Comprehensive Review for Stroke Nursing book.
When you purchase the SCRN Exam Prep Package, you save $60 compared to buying the products individually.
1. SCRN Review Course: The SCRN Review Course is a web-based review that includes interactive videos and quizzes to help you prepare for the SCRN exam or to review stroke nursing best practices. The online course is a self-paced study tool, earning 8 CE hours upon completion and evaluation of the course. Based on the Comprehensive Review for Stroke Nursing book and the SCRN exam content outline, the course includes ten modules. Key interactive features include progress checks, correct answer descriptions, and videos to emphasize key concepts.
2. SCRN Self-Assessment Exam: The self-assessment examination is 75 questions in length and parallels the actual SCRN examination content outline.
3. AANN Comprehensive Review for Stroke Nursing book: The AANN Comprehensive Review for Stroke Nursing is a comprehensive resource for nurses preparing for the SCRN exam and those looking for a review of current best practices for providing stroke care. This evidence-based book covers key topics listed on the SCRN exam blueprint, including anatomy and physiology, preventative care, hyperacute care, stroke diagnostics, acute care, medications, postacute care, and systems and quality care.


1. A person, typically male, who is very attractive to women. Often used with a degree of humor or facetiousness. Primarily heard in US, South Africa. John thinks he's such a babe magnet now that he's making so much money, but everyone thinks he's just as foolish as ever.
2. Something that attracts women to its owner, who is typically male. Often used with a degree of humor or facetiousness. Primarily heard in US, South Africa. I bought my motorcycle because I thought it would be a great babe magnet.
A person who is gullible, naïve, or lacks experience in a specific situation. Although Jane had always excelled in school, she felt like a babe in the woods when she began attending college.
Children, though inexperienced, are capable of saying wise, insightful, or mature things. So we're in the museum and my five-year-old says to this guy, "Hey, mister! No touching! Can't you read the sign?" Out of the mouths of babes, huh? A: "It's OK, Mommy. I know you're just upset because you're tired." B: "Thank you, sweetheart. Out of the mouths of babes."
1. Blameless or faultless. I swear, I didn't pull the fire alarm—I'm innocent as a newborn babe!
2. Unsophisticated, inexperienced, and/or gullible. This is her first job—she's as innocent as a newborn babe.
Fig. a naive or innocent person; an inexperienced person. (Like a child lost in the woods.) Bill is a babe in the woods when it comes to dealing with plumbers. As a painter, Mary is fine, but she's a babe in the woods as a musician.
1. guiltless. (*Also: as ~.) "Hey! You can't throw me in jail," cried the robber. "I'm innocent as a lamb."
2. naive; inexperienced. (*Also: as ~.) She's eighteen years old, but innocent as a newborn babe.
Out of the mouths of babes (oft times come gems).
Prov. Children occasionally say remarkable or insightful things. Mr. and Mrs. Doyle were quietly bickering in the kitchen when their seven-year-old daughter came in and said, "You guys should get counseling." After a surprised pause, Mrs. Doyle remarked, "out of the mouths of babes." Child: Don't eat so much candy, Mommy. Candy is bad for your teeth. Mother: out of the mouths of babes oft times come gems.
An innocent or very naive person who is apt to be duped or victimized, as in She was a babe in the woods where the stock market was concerned. The term originated in a popular ballad of 1595, "The Children in the Wood," about two young orphans who are abandoned in a forest and die.
Young and inexperienced persons often can be remarkably wise, as in She's only six but she said, quite rightly, that Harry was afraid of the sitter-out of the mouths of babes, Mother said . This expression is a shortening and revision of expressions in the Old and New Testaments of the Bible. In Psalms 8:2, God ordains strength out of the mouth of babes and sucklings; in Matthew 21:16, praise comes from this source. Later generations changed strength and praise to wisdom.
You call someone a babe in the woods if they have little experience of life, and they are involved in a difficult situation that they do not understand. By this time I wasn't such a babe in the woods, and one thing I insisted on was that they provide a contract. Note: `Babe' is an old-fashioned word for a baby or small child. An old story tells of two young orphans (= children with no parents) who were left in the care of their uncle. If the children died, the uncle would get their money. The uncle ordered a servant to take them into a wood where they died and their bodies were covered with leaves by the birds. There is a pantomime (= play for children) based on this story.
inexperienced people in a situation calling for experience.
The babes in the wood are characters in an old ballad The Children in the Wood, which dates from the 16th century. The two children are abandoned in the wood by their wicked uncle who wishes to steal their inheritance. The children die of starvation and robins cover their bodies with leaves; the uncle and his accomplice are subsequently brought to justice.
(American English) somebody who lacks experience of life or knowledge and who is too willing to trust other people: We're still babes in the woods when it comes to computer technology.This comes from an old song The Children in the Woods in which two children are left alone in the woods by a man who was paid to kill them.
1. and babes n. a term of endearment for a woman or a man. (see also baby.) Look, babe, get in there and tackle that guy! We're losing! Hey, babes, let's us two get this done and head on home.
2. n. a good-looking woman. Who is that babe standing on the corner over there? I saw you with that orange haired babe last night. What's the story?
1. n. a male who seems to attract good looking females easily. Keep your woman away from Chuck. He's a babe magnet. Wilfred thinks he's a chick magnet, but he only snags the dogs.
2. n. something, such as a car, that attracts good-looking females to a male. Man, I like your new chick magnet. Get any yet? He thought of a new car as a "babe magnet," but all it drew was the repo man.
Replying to Sturgeon, a Twitter user by the name of Tod said: "My daughter went though all phases from dolly babe to goth.
A spokesman said: "When Babe arrives at Hogget's Farm he is taken in by the trusty sheepdog Fly, and soon discovers a talent for herding.
When necessary, Babe would go beyond the call of duty.
Tavano said the neither the company, Production Marketing Services, out of Wakefield, nor the Babe Ruth league are doing anything illegal.
The second period (1984-92) of filmic imagery of Babe Ruth can be considered the negative reaction to his earlier sacralization.
Babe Didrikson was born on June 26, 1914, in Port Arthur, Texas, the sixth of seven children.
Darlings of the independent music press for the past two years on the strength of their first full-length release, Desperate Youth, Blood Thirsty Babes (Touch and Go, 2004), TV On The Radio got a huge public co-sign by rock legend David Bowie and then experienced the typical indy-favorite-signs-with-major-label backlash following their switch to pop juggernaut Interscope Records.
In 1927, Babe Ruth's 60 home runs accounted for 14 percent of the American League's total that year.
Baseball followers have been resigned to this happening since Barry showed up in spring training in something approximating health, six home runs shy of the Babe.
From beyond the grave, Aunt Babe supernaturally counsels and guides Bernita to move on.
The name refers to the "Curse of the Bambino," which has kept the Boston Red Sox from winning the World Series for 85 years, dating back to the time the team's owner sold Babe Ruth to the New York Yankees.
Pop beauty and ex-Brookie babe Jennifer, 21, was stunner-up with EastEnders' star Jill Halfpenny, 28, third.
The longest home run of Babe Ruth's incredible hitting career was not struck at any major league ballpark.


text:
Scammers claiming to have COVID-19 results | #coronavirus | #scams | #covid19
COLLIER COUNTY, Fla. — Day after day, people living in Collier county are lining up to get tested for COVID-19. There's now a handful of walk-up sites in Immokalee, too. It's labeled a hot spot with the highest number of positive cases in Florida at 1,172.
Kristine Hollingsworth with the Collier Dept. of Health says many people are anticipating their results.
"With the increased testing there are more people waiting their results and they're waiting for that phone call," she said.
Scammers are taking advantage of that. Holingsworth says they're posing as DOH workers calling to share test results, but the call is full of red flags.
"They're asking for a full social security number for example. They're also asking questions about medical history," said Hollingsworth.
Questions she says the DOH would never ask over the phone.
Bryan Oglesby with the Better Business Bureau says this is just one of dozens of schemes scammers are using to steal from you.
"Their intent is to either steal your identity, by social security number. Maybe a credit card number to perpetrate credit card fraud," he said.
Hollingsworth says DOH will verify your identity in other ways before sharing your test results.
"We will ask to verify your name, your address, and your date of birth," she said. "If you receive a call like this, hang up and contact the Department of Health at 239-252-8200."
You can also report it to the Collier County Sheriff's Office and the BBB scam tracker.
Oglesby also says watch out for scammers pretending to be contact tracers. If they ask for anything other than your date of birth or address, it's likely a scam.


THE MONA LISA WILL BE A 4-STAR LUXURY CONDO HOTEL. IT WILL CONSIST OF 2 5-STORY BUILDINGS SPREAD OVER 12 ACRES. THERE WILL BE 240 ONE AND TWO BEDROOM UNITS.
CONSTRUCTION WILL BEGIN JAN 2006 AND IS EXPECTED TO BE COMPLETE BY APRIL 2007.
PRICES STARTING AT $324,000 TO $534,000.