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Description

The Cohen’s cumulative distance Workflow provides an environment to calculate the Cohen’s cumulative distance. The Cohen’s cumulative distance measures the difference between observed and expected vectors along the matrix path that the population would take to reach the expected population vector. It is a function of both the observed stage distribution (n0) and the structure of the matrix (A) (Williams et al 2011). Cohen’s cumulative distance will not work for reducible matrices or imprimitive matrices with nonzero imaginary components in the dominant eigenpair, and returns a warning for other imprimitive matrices (Caswell 2001).

 

Biovel Portal Tutorial

To run this workflow in the Biovel Portal please refer to Tutorial Manual 

 

General

Name of the workflow and its myExperiment identifier

Name: Cohen’s cumulative distance

myExperiment pack 651 or only the workflow: myExperiment workflow 4346

Date, version and licensing

Last updated: 23th July 2014

Version: 1

Licensing: CC-BY-SA

How to cite this workflow

To report work that has made use of this workflow, please add the following credit acknowledgement to your research publication:

The input data and results reported in this publication (tutorial) come from data (Dr. Gerard Oostermeijer unpublished results and publication: Oostermeijer, J.G.B. M.L. Brugman, E.R. de Boer; H.C.M. Den Nijs. 1996. Temporal and Spatial Variation in the Demography of Gentiana pneumonanthe, a Rare Perennial Herb. The Journal of Ecology, 84: 153-166.) using BioVeL workflows and services (www.biovel.eu). Cohen’s cumulative distance workflow was run on <date of the workflow run>. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359.

 

Scientific specifications

Keywords

Matrix Population Models, BioVeL, Cohen’s cumulative distance demography, Gentiana pneumonanthe, matrix, matrix population models, population, stable stage distribution (w), stage matrix.

Scientific workflow description

The Cohen’s cumulative distance Workflow provides an environment to calculate the Cohen’s cumulative distance. The workflow accepts input data in a .txt format. The output is provided as a set of R results.

The Cohen’s cumulative distance measures the difference between observed and expected vectors along the matrix path that the population would take to reach the expected population vector. 

 

Technical specifications

The Workflow requires a Taverna Engine. The simplest way to install a Taverna Engine is to install Taverna Workbench. The workflow also requires an Rserve installation with popdemo package installed. It is possible to setup the workflow to use a remote Rserve. However, instructions for installing a local Rserve are provided below.

Dependencies

Install R software in your computer. See: http://www.r-project.org/

How it works

First, open R, once R is opened, type library(Rserve) and press enter; then type Rserve() and press enter again. You will see then something similar to the following message:


After this operation you can open Taverna and run the workflow.

 

Bibliography

This workflow was created using and based the package popdemo (Stott, Hodgson and Townley, 2013)

  • Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts.
  • Jongejans E. & H. de Kroon. 2012. Matrix models. Chapter in Encyclopaedia of Theoretical Ecology (eds. Hastings A & Gross L) University of California, p415-423
  • Oostermeijer J.G.B., M.L. Brugman; E.R. de Boer; H.C.M. Den Nijs. 1996. Temporal and Spatial Variation in the Demography of Gentiana pneumonanthe, a Rare Perennial Herb. The Journal of Ecology, Vol. 84(2): 153-166.
  • Stott, I., D.J. Hodgson and S. Townley. 2013. popdemo: Provides Tools For Demographic Modelling Using Projection Matrices. Version 0.1-3.
  • Williams, J.L., M.M. Ellis, M.C. Bricker, J.F. Brodie and E.W. Parsons. 2011. Distance to stable stage distribution in plant populations and implications for near-term population projections. Journal of Ecology, 99, 1171–1178.

 

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