Model of material and financial flows in the forest industry cluster of the Tomsk region

The main objective of the paper is the modeling of the timber industry complex in the Tomsk region from circular economy and sustainable development point of view. On the basis of the value chain process and the formation of threats along the whole chain nature (forest) - economy - society (consumer) the modeling of products and waste creation has been developed. The large amount of unused waste requires the development of a special regional forestry cluster model using circular economy approach in order to propose best solutions for recycling

Inferring Behavioral States of Grazing Livestock from High-Frequency Position Data Alone

<div><p>Studies of animal behavior are crucial to understanding animal-ecosystem interactions, but require substantial efforts in visual observation or sensor measurement. We investigated how classifying behavioral states of grazing livestock using global positioning data alone depends on the classification approach, the preselection of training data, and the number and type of movement metrics. Positions of grazing cows were collected at intervals of 20 seconds in six upland areas in Switzerland along with visual observations of animal behavior for comparison. A total of 87 linear and cumulative distance metrics and 15 turning angle metrics across multiple time steps were used to classify position data into the behavioral states of walking, grazing, and resting. Five random forest classification models, a linear discriminant analysis, a support vector machine, and a state-space model were evaluated. The most accurate classification of the observed behavioral states in an independent validation dataset was 83%, obtained using random forest with all available movement metrics. However, the state-specific accuracy was highly unequal (walking: 36%, grazing: 95%, resting: 58%). Random undersampling led to a prediction accuracy of 77%, with more balanced state-specific accuracies (walking: 68%, grazing: 82%, resting: 68%). The other evaluated machine-learning approaches had lower classification accuracies. The state-space model, based on distance to the preceding position and turning angle, produced a relatively low accuracy of 64%, slightly lower than a random forest model with the same predictor variables. Given the successful classification of behavioral states, our study promotes the more frequent use of global positioning data alone for animal behavior studies under the condition that data is collected at high frequency and complemented by context-specific behavioral observations. Machine-learning algorithms, notably random forest, were found very useful for classification and easy to implement. Moreover, the use of measures across multiple time steps is clearly necessary for a satisfactory classification.</p></div

Variable importance of movement metrics in model C.

<p>Mean variable importance of movement metrics for the behavioral states walking (A), grazing (B), and resting (C) as calculated from 50 random forest realizations of model C. Colors and symbols indicate the different movement metrics cumulative distance (orange), linear distance (violet), forward distance (upward triangle), backward distance (downward triangle), mean distance (point), turning angle and mean turning angles over multiple positions (green square).</p

Comparison of observed and predicted behavioral states along an exemplary cow track.

<p>Colors indicate the three behavioral states of walking (gold), grazing (blue), and resting (red) as observed in the field (Observation) and as predicted by eight classification models (A - H). Gray symbols are observed additional states, which were too rare for classification. Slight jitter was added to the positions to minimize symbol overlap. The time interval between positions is 20 s, except for model E, subsampled to 60 s. Gray lines are isolines at 2 m distance (swissALTI3D, Swiss Federal Office of Topography, Wabern). The inlay shows the confusion matrix with gray-shading according to the percentage of positions in each row classified into the three behavioral states, i.e. the first row shows the percentage of positions observed as grazing and classified as walking (w), grazing (g), and resting (r).</p

Distributions of movement speed and turning angle at 20 s and 60 s time intervals.

<p>Density distributions within the observed behavioral states walking (gold), grazing (blue), and resting (red), and in the data set of the test of GPS device accuracy (gray). Continuous lines represent the original data with 20 s intervals, dotted lines represent the data subsampled to 60 s intervals.</p

Classification accuracies of the models A – H and the null model.

<p>Percentage of correctly classified states in joint sequences together with 50% and 95% quantile intervals for individual sequences, overall (black) and for the three behavioral states walking (gold), grazing (blue) and resting (red). For specifications of models see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114522#pone-0114522-t001" target="_blank">Table 1</a>.</p

Relative abundance of the three behavioral states.

<p>Accumulated relative abundance of the states walking (gold), grazing (blue), and resting (red), as it was observed (Ob) and as it was predicted by the eight classification models (A - H) in the example track (striped columns) and in the whole data set (filled columns).</p

Schematic representation of movement metrics used as predictor variables in the classifications.

<p>Movement metrics include backward cumulative distance (a1), forward cumulative distance (a2), backward linear distance (b1), forward linear distance (b2), and turning angle between GPS positions (c).</p