Parametric_BootstrapstageMatrixFile00 Here comes the stage matrix without the stage names (as you see in the example). It should be provied as a txt-file. Example from: J. Gerard B. Oostermeijer; 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. 2014-06-18 08:53:16.32 UTC 0.0000 0.0000 0.0000 7.6660 0.0000 0.0579 0.0100 0.0000 8.5238 0.0000 0.4637 0.8300 0.9009 0.2857 0.8604 0.0000 0.0400 0.0090 0.6190 0.1162 0.0000 0.0300 0.0180 0.0000 0.0232 2014-06-06 13:48:03.530 UTC abundances11 In this dialogue appear the fields with the initial abundance per stage observed in the field. As an example Gentiana pneumonanthe has 5 stages with its respective abundance. The abundance of the stages or categories must be added one by one. First press Add value, fill the abundance of the first stage and press enter; then press Add value and fill once again the next stage abundance; repeat the action until you have fill all the abundance of all stages. Stage abundance of the year 1987: 1) S (seedlings) 69 2) J (Juveniles) 100 3) V (vegetative) 111 4) G (reproductive individuals) 21 5) D (dormant plants) 43 2014-06-18 08:53:30.104 UTC [69, 100, 111, 21, 43] 2014-06-06 13:48:49.284 UTC iterations00 Number of iterations for calculation of the resample analysis of the parametric Bootstrap. Click in Set Value, tip the number in the right window. 2014-06-18 08:53:35.815 UTC 10000 2014-06-06 13:47:15.976 UTC stages11 [S, J, V, G, D] 2014-06-06 13:49:16.711 UTC Here come the names of the stages or categories of the input matrix. It is very important that the stages names are not longer than 8 characters. The name of the stages must be added one by one. The respective name stages must be filled one by one. First press add value, fill a stage name (not longer than 8 characters) and press enter, then press add value and fill once again the next stage name, repeat the action until you have fill all the stages names. In the following example, the matrix has 5 stages or categories: S J V G D S 0.0000 0.0000 0.0000 7.6660 0.0000 J 0.0579 0.0100 0.0000 8.5238 0.0000 V 0.4637 0.8300 0.9009 0.2857 0.8604 G 0.0000 0.0400 0.0090 0.6190 0.1162 D 0.0000 0.0300 0.0180 0.0000 0.0232 The stages of this matrix are called: 1) Seedlings S 2) Juveniles J 3) Vegetative V 4) Reproductive individuals G 5) Dormant plants D 2014-06-18 08:53:54.468 UTC plotTitle00 Descriptive title for labelling generated output graph. Click in Set Value, tip the text in the right window. e.g.: Gentiana pneumonanthe, Terschelling (species taxonomic name, place where the research was conducted) 2014-06-18 08:53:42.306 UTC Gentiana pneumonanthe, Terschelling 2014-06-06 13:47:39.32 UTC histogram0 Histogram plotting the frequencies of the lambda values and the 95% confidence intervals resulting from the bootstrap analysis. 2014-06-06 14:36:29.892 UTC lambda0 1.2375 2014-06-06 14:37:00.728 UTC Lambda (λ) or dominant eigenvalue, this value describes the population growth rate of a stage matrix. The population will be stable, grow or decrease at a rate given by lambda: e.g.: Lambda (λ) = 1 (population is stable), Lambda (λ) > 1 (population is growing) and finally Lambda (λ) < 1 (population is decreasing). 2014-06-18 08:54:57.109 UTC x0 X= List of resampled matrices it is the list of matrices generated by bootstrap analysis and the number of the matrices at the same time is based on the number of itineration’s. 2014-06-06 14:37:55.419 UTC mean0 The mean matrix is calculated based on the matrices generated by bootstrap analysis and the number of the matrices at the same time is based on the number of itineration’s. 2014-06-18 08:55:16.552 UTC variance0 The variance matrix is calculated based on the matrices generated by bootstrap analysis and the number of the matrices at the same time is based on the number of itineration’s. 2014-06-18 08:55:24.883 UTC confidence_interval0 95% Confidence interval of Lambda. In statistics, a confidence interval (CI) is a type of interval estimate of a population parameter and is used to indicate the reliability of an estimate. It is an observed interval (i.e. it is calculated from the observations), in principle different from sample to sample, that frequently includes the parameter of interest if the experiment is repeated. How frequently the observed interval contains the parameter is determined by the confidence level or confidence coefficient 2014-06-18 08:54:05.420 UTC 2.5% 97.5% 0.9617 1.4765 2014-06-06 14:36:02.587 UTC displayinput1output00net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity input 1 false output 0 0 false localhost 6311 false false input R_EXP output TEXT_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokeplot_histogramci1plottitle0y1output00net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity y 1 false plottitle 0 false ci 1 false output 0 0 false localhost 6311 false false y R_EXP plottitle STRING ci R_EXP output PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokedisplay_2input1output00net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity input 1 false output 0 0 false localhost 6311 false false input R_EXP output TEXT_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokedisplay_3input1output00net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity input 1 false output 0 0 false localhost 6311 false false input R_EXP output TEXT_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokedisplay_4input1output00net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity input 1 false output 0 0 false localhost 6311 false false input R_EXP output TEXT_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeWorkflow32iterations0abundances1stage_matrix1frows1fcols1confidence_interval11x11mean11y11variance11lambda00net.sf.taverna.t2.activitiesdataflow-activity1.5net.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeStageMatrix_ReadFromFilestages1stageMatrixFile0stageMatrix11net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity stageMatrixFile 0 false stages 1 false stageMatrix 1 1 false localhost 6311 false false stageMatrixFile TEXT_FILE stages STRING_LIST stageMatrix R_EXP net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeCategoriseStages_InteractionunsortedStages1sortedStages11reproductiveStages11recruitedStages11 With this dialogue automatically appears the names of the stages or categories of the census data file. When the dialogue appears, the stages are in disorder, so the user drags and organizes the stages according to the order in the life cycle. Then, the author chooses if the stage belongs to the recruited, reproductive category or it should be excluded. Recruited means that new individuals can be recruited to this (these) stage(s). Reproductive stages are those that reproduce (produce offspring) (in this example the stage G). In the census data file Dt1.txt, x is use to denote when a plant has died in the second year, so the user must selected in the excluded column. Then the user clicks in confirm and you will read stages submitted. In the following example, the life cycle of Gentiana pneumonanthe has 5 stages or categories: 1) Seedlings S 2) Juveniles J 3) Vegetative V 4) Reproductive individuals G 5) Dormant plants D 2012-11-01 14:53:35.21 UTC net.sf.taverna.t2.activitiesinteraction-activity1.5net.sf.taverna.t2.activities.interaction.InteractionActivity unsortedStages 1 text/plain java.lang.String false sortedStages 1 1 recruitedStages 1 1 reproductiveStages 1 1 http://biovel.googlecode.com/svn/trunk/popmod/mpm/select_stages.html LocallyPresentedHtml false net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeFecundityCols_FromReproductiveStagesall_values1some_values1indices11net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity some_values 1 false all_values 1 false indices 1 1 false localhost 6311 false false some_values STRING_LIST all_values STRING_LIST indices INTEGER_LIST net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeFecundityRows_FromRecruitedStagesall_values1some_values1indices11net.sf.taverna.t2.activitiesrshell-activity1.5net.sf.taverna.t2.activities.rshell.RshellActivity some_values 1 false all_values 1 false indices 1 1 false localhost 6311 false false some_values STRING_LIST all_values STRING_LIST indices INTEGER_LIST net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5net.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokedisplayinputWorkflow32confidence_intervalplot_histogramciWorkflow32confidence_intervalplot_histogramplottitleplotTitleplot_histogramyWorkflow32ydisplay_2inputWorkflow32xdisplay_3inputWorkflow32meandisplay_4inputWorkflow32varianceWorkflow32iterationsiterationsWorkflow32abundancesabundancesWorkflow32stage_matrixStageMatrix_ReadFromFilestageMatrixWorkflow32frowsFecundityRows_FromRecruitedStagesindicesWorkflow32fcolsFecundityCols_FromReproductiveStagesindicesStageMatrix_ReadFromFilestagesstagesStageMatrix_ReadFromFilestageMatrixFilestageMatrixFileCategoriseStages_InteractionunsortedStagesstagesFecundityCols_FromReproductiveStagesall_valuesCategoriseStages_InteractionsortedStagesFecundityCols_FromReproductiveStagessome_valuesCategoriseStages_InteractionreproductiveStagesFecundityRows_FromRecruitedStagesall_valuesCategoriseStages_InteractionsortedStagesFecundityRows_FromRecruitedStagessome_valuesCategoriseStages_InteractionrecruitedStageshistogramplot_histogramoutputlambdaWorkflow32lambdaxdisplay_2outputmeandisplay_3outputvariancedisplay_4outputconfidence_intervaldisplayoutput 5ddec6d6-2ef8-4a1b-8905-4ebc313e8a5f 2012-03-30 15:53:45.576 UTC c666682d-b97f-4ae5-a33a-2c2dbd8cc73a 2012-06-27 09:44:02.36 UTC 50e537db-c0d0-478d-b55b-5b53de52ec3d 2012-12-10 15:51:50.369 UTC Maria Paula Balcázar-Vargas, Jonathan Giddy and Gerard Oostermeijer 2014-06-06 13:46:10.288 UTC 0c1873c5-fc98-40c8-960c-bd11f9060072 2012-06-27 09:34:26.127 UTC d3856b08-20d7-4f7d-8d0f-7cefebd654f0 2012-06-08 05:56:58.172 UTC 1bc8b332-e0fa-4c1b-9693-2e7b0297d635 2012-06-08 07:34:34.886 UTC f364089f-de01-4c6d-b4e8-3e0f55f8952f 2012-06-08 06:30:14.837 UTC e4416b66-0517-4982-9ffc-07d9225b4cb4 2012-04-27 10:20:27.369 UTC 26a58a12-7058-4ecc-8626-8f4208df050d 2012-04-27 06:37:14.879 UTC 87080ba6-c0da-4929-875b-a334cd6c80dc 2012-07-04 15:48:37.846 UTC 23b013c9-2e87-45bc-890e-3d5def9f9a83 2014-06-06 13:47:40.696 UTC 60c89fa1-3c42-4a86-98d4-d6aaf4dc5b36 2012-06-08 06:21:13.990 UTC 9980e502-edf8-45be-b834-da4a69c1aa6f 2012-06-27 09:38:41.13 UTC 3da39227-d90d-44b6-93e6-a5c6c6635c28 2012-03-30 15:42:09.674 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09f91d33-9f50-4983-96c9-1335ed1f92b4 2012-04-27 06:19:38.161 UTC dcabfdeb-51a1-4ac8-91d0-f50ef43f28bb 2012-06-08 07:04:17.60 UTC 940fe2e3-58d4-475d-8bcf-3ede6bf0dabb 2012-06-08 07:47:49.992 UTC c1c17ab3-ff6e-4202-90f2-826971327340 2012-06-27 08:45:51.822 UTC cfe0f676-d994-4cf6-9af7-e7b8b4ce6083 2012-04-27 10:33:07.555 UTC f8a070c5-cf92-4946-85c2-d7d88d5517c1 2014-06-06 14:36:31.112 UTC 6ad4d84d-6c33-4a35-9142-512f206419d2 2012-07-04 15:55:59.146 UTC 482df01a-aece-421a-87e6-87c6fed3098c 2012-04-27 06:16:05.128 UTC The Parametric Bootstrap or Resample a projection matrix Workflow provides an environment to resample a projection matrix using a multinomial distribution for transitions and a log normal distribution for fertilities (Stubben, Milligan, and Nantel. 2011). The resample is based on number of plants surveyed. The projection matrix A is first split into separate transition and fertility matrices. Dead fates are added to the transition matrix and the columns are then sampled from a Multinomial distribution based on the size in each corresponding stage class in n. The fertility rates are sample from a Log Normal distribution using the lnorms function. The same variance is applied to all rates by default. (Stubben, Milligan and Nantel 2013, Caswell 2001 see section 12.1.5.2). The goal of a demographic analysis is very often to estimate lambda, because lambda is estimated from imperfect data, such estimation are uncertain. Therefore, when the results have policy implications it is important to quantify that uncertainty. Confidence interval is one of the traditional tools to doing so (see outputs: Confidence interval of Lambda). Analyses: • Lambda (λ) • Mean matrix • Variance matrix • Histogram • Confidence interval of Lambda • X= List of resampled matrices ============================================================ This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359. This workflow was created based on Package ‘popbio’ in R. Stubben, C & B. Milligan. 2007. Estimating and Analysing Demographic Models Using the popbio Package in R. Journal of Statistical Software 22 (11): 1-23 Stubben, C., B. Milligan, P. Nantel. 2011. Package ‘popbio’. Construction and analysis of matrix population models. Version 2.3.1 ============================================================ For further details see: Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts. Stott, I., S. Townley and D.J. Hodgson 2011. A framework for studying transient dynamics of population projection matrix models. 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