Fig 1 -

(a)-(b) The Picea glauca Pixie specimen, alias ‘Pixie Tree’. (c)-(d) The Picea glauca Cy’s Wonder specimen, alias ‘Wonder Tree’. (e)-(f) The Picea abies Tompa specimen, alias ‘Tompa Tree’. (a) Skeletal branching pattern of Pixie Tree (obtained by CT scanning). (b) Analytical representation of the branching pattern for Pixie Tree. (c) Skeletal branching pattern of Wonder Tree (obtained by CT scanning). (d) Analytical representation of the branching pattern for Wonder Tree. (e) Skeletal branching pattern of Tompa Tree (obtained by CT scanning). (f) Analytical representation of the branching pattern for Tompa Tree.</p

S1 Appendix -

(PDF)</p

The <i>R</i>² values for the selected modes of Table 4, including interaction effects presented in the supplementary material.

The R2 values for the selected modes of Table 4, including interaction effects presented in the supplementary material.</p

Summary statistics of Pixie Tree, Wonder Tree and Tompa Tree (in this order, from top to bottom in each cell of the table) for the variables defined in Table 2.

Summary statistics of Pixie Tree, Wonder Tree and Tompa Tree (in this order, from top to bottom in each cell of the table) for the variables defined in Table 2.</p

Description of the variables used in Section 4.

Description of the variables used in Section 4.</p

The zipped file DMRSuppMat contains the analytical representations for the three trees condidered in this work.

It also contains an R script, basic_example.R, for fitting length models of Pixie Tree, thus reproducing the results presented in Table 12 in S1 Appendix. (ZIP)</p

The data entries for a level 4 branch of Pixie Tree.

The data entries for a level 4 branch of Pixie Tree.</p

Capture-Recapture Methods for Data on the Activation of Applications on Mobile Phones

<p>This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can create a capture-recapture dataset about devices, that is, users, that “visited” the business: the units are owners of mobile devices and the capture occasions are time intervals such as days. A unit is captured when she activates an application, provided that this activation is recorded by the platform providing the data. Statistical capture-recapture techniques can be applied to the app data to estimate the total number of users that visited the business over a time period, thereby providing an indirect estimate of foot traffic. This article argues that the robust design, a method for dealing with a nested mark-recapture experiment, can be used in this context. A new algorithm for estimating the parameters of a robust design with a fairly large number of capture occasions and a simple parametric bootstrap variance estimator are proposed. Moreover, new estimation methods and new theoretical results are introduced for a wider application of the robust design. This is used to analyze a dataset about the mobile devices that visited the auto-dealerships of a major auto brand in a U.S. metropolitan area over a period of 1 year and a half. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.</p

Directional analysis of bison trails with respect to directional persistence (Direction. P.), the target meadow (TM), and the nearest canopy gap (CG).

<p>Directional analysis of bison trails with respect to directional persistence (Direction. P.), the target meadow (TM), and the nearest canopy gap (CG).</p

Capture-Recapture Methods for Data on the Activation of Applications on Mobile Phones

<p>This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can create a capture-recapture dataset about devices, that is, users, that “visited” the business: the units are owners of mobile devices and the capture occasions are time intervals such as days. A unit is captured when she activates an application, provided that this activation is recorded by the platform providing the data. Statistical capture-recapture techniques can be applied to the app data to estimate the total number of users that visited the business over a time period, thereby providing an indirect estimate of foot traffic. This article argues that the robust design, a method for dealing with a nested mark-recapture experiment, can be used in this context. A new algorithm for estimating the parameters of a robust design with a fairly large number of capture occasions and a simple parametric bootstrap variance estimator are proposed. Moreover, new estimation methods and new theoretical results are introduced for a wider application of the robust design. This is used to analyze a dataset about the mobile devices that visited the auto-dealerships of a major auto brand in a U.S. metropolitan area over a period of 1 year and a half. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.</p