Matrix_Population_Molabel00 Descriptive title for labelling generated outputs. 2012-11-01 12:14:05.640 UTC Gentiana pneumonanthe, Terschelling 2012-10-23 15:39:06.31 UTC shortTermYears00 This value will be use to plot a graph that shows a simulation of the number of individuals per stage a few years after the study. This value represents the years of axis X of the output graph: StageVectorPlotShortTerm. 2012-11-01 11:51:21.500 UTC 10 2012-10-29 13:44:13.835 UTC longTermYears00 This value contains the maximum number of iterations in the transient dynamic analysis, and hence the total number of years for the long term graphs. The number of years will be used in two output graphs: 1) StageVectorPlotLongTermProportional: the proportion of individuals per stage in the long term. 2) StageVectorPlotLongTermLogarithmic: the number of individuals per stage in the long term. 2012-11-01 11:51:48.46 UTC 50 2012-10-29 13:44:37.435 UTC censusDataFile00 The input data file must have the format of a table containing the demographic data on a series of individuals over several years. Each individual has a table row for each year and is identified by a number (plant column in example). For the year specified in the column 'year', each individual has a certain life stage ('stage'). The stage codes can be chosen by the user, but they cannot be longer than 8 characters. For all individuals, the number of offspring is specified in a chosen way (in the example, 'reprstr' gives the number of flowers/fruits for each plant. In the example, it can be seen that only generative adults (stage=G) have flowers. Individuals without offspring must be indicated by filling in '0' in this column (i.e. the cannot have empty cells). The column 'recruitment' specifies those individuals that are new in the second year census to the population by means of a code. In the example, 'RS' is a new seedling, and 'RJ' a new juvenile plant. As can be seen in the example, no code is needed in this column for individuals that were already present. 2014-07-03 14:13:48.628 UTC plant year stage reprstr recruitment 2 1987 D 0 4 1987 J 0 5 1987 D 0 31 1987 V 0 36 1987 G 5 37 1987 J 0 41 1987 J 0 43 1987 S 0 46 1987 V 0 2 1988 V 0 4 1988 D 0 5 1988 V 0 9 1988 J 0 RJ 14 1988 J 0 RJ 2014-07-03 14:14:00.64 UTC iterations00 10000 2012-12-10 16:16:31.915 UTC Number of iterations for calculation of confidence interval 2012-12-10 16:16:40.761 UTC stable_stage_distribution1 A bar plot which shows the stable stage distribution (w) of the analysed matrix. In other words, the proportion of individuals per stage. 2012-11-01 12:16:09.906 UTC projection_matrix1 The stage matrix, displayed with coloured squares to highlight the magnitude of the values. 2012-11-01 10:39:02.187 UTC eigenanalysis_elasticity_matrix1 The elasticity matrix, displayed with coloured squares to highlight the magnitude of the values. 2012-11-01 12:16:26.656 UTC eigenanalysis_sensitivity_matrix_11 A sensitivity matrix showing the sensitivities of the actual transitions (i.e. the sensitivity values of non-zero elements), displayed with coloured squares to highlight the magnitude of the values. 2012-11-01 10:40:25.78 UTC eigenanalysis_sensitivity_matrix_21 A sensitivity matrix showing the sensitivities of all possible transitions (i.e. the sensitivity values of zero and non-zero elements), displayed with coloured squares to highlight the magnitude of the values. 2012-11-01 10:41:08.312 UTC stage_matrix1 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 2012-11-01 10:16:15.78 UTC A stage matrix contains transitions probabilities from each stage to the next. In the example, the selected species, Gentiana pneumonanthe, has a matrix of 5 x 5 stages (Oostermeijer et al., 1996). 2012-11-01 10:14:42.968 UTC eigenanalysis1 The Eigen analysis results are a set of demographic statistics: Lambda 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.: λ = 1 (population is stable), λ > 1 (population is growing) and finally λ < 1 (population is decreasing). The stable stage distribution (w): It is the proportion of the number of individuals per stage. It is given analytically by the right eigenvector (another property of the transition matrix) that corresponds to the dominant eigenvalue Elasticity and Sensitivity: Sensitivity and elasticity analyses are prospective analyses. The sensitivity matrix: The sensitivity gives the effect on λ of changes in any entry of the matrix, including those that may, at a given context, be regarded as fixed at zero or some other value. The derivative tells what would happened to λ if aij was to change, not whether, or in what direction, or how much, aij actually change. The hypothetical results of such impossible perturbations may or may not be of interest, but they are not zero. It is up to you to decide whether they are useful (Caswell 2001). When comparing the λ-sensitivity values for all matrix elements one can find out in what element a certain increase has the biggest impact on λ. However, a 0.01 increase in a survival matrix element is hard to compare to a 0.01 increase in a reproduction matrix element, because the latter is not bound between 0 and 1 and can sometimes take high values. Increasing matrix element a14 (number of S (seedlings) the next year produced by a G (Reproductive individuals)) with 0.01 from 7.666 to 7.676 does not have a noticeable effect on λ. For comparison between matrix elements it can therefore be more insightful to look at the impact of proportional changes in elements: by what percentage does λ change if a matrix element is changed by a certain percentage? This proportional sensitivity is termed elasticity (Description based on Oostermeijer data, based on Jongejans & de Kroon 2012). The Elasticity matrix: The elasticities of λ with respect to the stage are often interpreted as the “contributions” of each of the stages to λ. This interpretation relies on the demonstration, by de Kroon et al (1986) that the elasticitis of the λ with respect to the stage, always sum to 1. For further information see: de Kroon, et al., 1986. and Caswell 2001. Reproductive value (v): scaled so v[1]=1. To what extent will a plant or animal of a determinate category or stage , contribute to the ancestry of future generation. The damping ratio: it can be considered as a measure of the intrinsic resilience of the population, describing how quickly transient dynamics decay following disturbance or perturbation regardless of population structure, the larger the p, the quicker the population converges. 2013-08-01 14:19:59.478 UTC $lambda1 [1] 1,232338 $stable.stage S J V G D 0,14218794 0,16166957 0,65944861 0,02285525 0,01383863 $sensitivities S J V G D S 0 0 0 0,006042076 0 J 0,14255579 0,1620878 0 0,02291438 0 V 0,08206359 0,0933074 0,3806 0,01319088 0,00798695 G 0 2,7675986 11,28901 0,391255815 0,2369016 D 0 0,3325676 1,35654 0 0,02846721 $elasticities S J V G D S 0 0 0 0,037589187 0 J 0,006706037 0,001315287 0 0,15406651 0 V 0,03088315 0,062844075 0,27823767 0,003058271 0,005576792 G 0 0,089832455 0,08252831 0,196541848 0,022353201 D 0 0,008096016 0,01983398 0 0,000537213 $repro.value S J V G D 1 3,792468 2,18317 64,755197 7,781288 $damping.ratio [1] 2,0902 2012-10-31 12:11:52.131 UTC short_term_stage_vector_plot1 A plot that charts the number of individuals per stage vs. years in the short-term (e.g. 5 or 10 years). The number of years is related to the short-term years input value. 2012-11-01 11:47:06.281 UTC long_term_logarithmic_stage_vector_plot1 The number of individuals per stage in the long term 2012-11-01 11:46:16.968 UTC population_projection1 Population projection: Lambda or dominant eigenvalue: The population will be stable, grow or decrease at a rate given by lambda: e.g.: λ = 1 (population is stable), λ > 1 (population is growing) and finally λ < 1 (population is decreasing). e.g. The projected population growth rate (λ) is 1.237, meaning that the population is projected to increase with 23% per year if these model parameters remain unchanged. The stable stage distribution (stable.stage): It is the proportion of the number of individuals per stage and it is given by (w). Stage vector (stage.vectors): it is the projection of the number of individuals per category per year in the long term, the long term was stipulated in the input port (long term years) (e.g. 50 years). Population sizes (pop.sizes): it is the total population size per year in the long-term (e.g. 50 years). Population change (pop.changes): are the lambda values per year in the long-term (e.g. 50 years). 2012-11-01 12:14:44.46 UTC $lambda [1] 1.232338 $stable.stage S J V G D 0.14218794 0.16166957 0.65944861 0.02285525 0.01383863 $stage.vectors 0 1 2 3 4 5 6 7 S 69 161 176.33333 187.22072 219.09046 261.92397 318.22637 389.44517 J 100 179 201.69476 214.57709 249.78014 298.27181 362.08839 442.95984 V 111 258 467.40332 685.98643 903.75302 1149.34660 1437.18683 1783.39662 G 21 23 24.42009 28.57702 34.16400 41.50779 50.79720 62.39105 D 43 6 10.15818 14.70876 19.13949 24.22235 30.22041 37.46071 8 9 10 11 12 13 14 S 478.33138 588.52367 724.70467 892.75361 1099.9811 1355.4346 1670.2865 J 543.96054 669.21317 824.03064 1015.09148 1250.7042 1541.1537 1899.1418 V 2204.99218 2721.56781 3356.41003 4137.71650 5099.9406 6285.3667 7746.0003 G 76.76396 94.52670 116.44612 143.47580 176.7958 217.8635 268.4762 D 46.29325 57.12499 70.44214 86.83496 107.0256 131.9009 162.5519 15 16 17 18 19 20 21 S 2058.3178 2536.5199 3125.836 3852.0783 4747.0575 5849.9764 7209.1464 J 2340.3370 2884.0582 3554.118 4379.8647 5397.4679 6651.5012 8196.8954 V 9545.8697 11763.8434 14497.093 17865.3551 22016.1771 27131.3836 33435.0416 G 330.8504 407.7177 502.445 619.1814 763.0404 940.3234 1158.7961 D 200.3221 246.8664 304.224 374.9074 462.0130 569.3564 701.6396 22 23 24 25 26 27 28 S 8884.1038 10948.218 13491.904 16626.585 20489.572 25250.078 31116.629 J 10101.3442 12448.269 15340.474 18904.649 23296.916 28709.674 35380.022 V 41203.2756 50776.363 62573.642 77111.875 95027.892 117106.479 144314.761 G 1428.0284 1759.814 2168.685 2672.553 3293.488 4058.691 5001.679 D 864.6572 1065.550 1313.118 1618.205 1994.175 2457.498 3028.468 29 30 31 32 33 34 35 S 38346.204 47255.483 58234.725 71764.863 88438.565 108986.20 134307.83 J 43600.144 53730.113 66213.658 81597.604 100555.825 123918.76 152709.79 V 177844.559 219164.602 270084.859 332835.826 410166.224 505463.41 622901.75 G 6163.759 7595.834 9360.634 11535.465 14215.591 17518.41 21588.61 D 3732.096 4599.204 5667.773 6984.612 8607.403 10607.23 13071.69 36 37 38 39 40 41 42 S 165512.64 203967.51 251356.91 309756.66 381724.90 470414.08 579709.13 J 188190.08 231913.78 285796.15 352197.45 434026.28 534867.07 659136.99 V 767625.48 945974.02 1165759.69 1436609.93 1770388.96 2181717.52 2688613.33 G 26604.46 32785.68 40403.04 49790.20 61358.36 75614.23 93182.29 D 16108.74 19851.41 24463.65 30147.49 37151.89 45783.69 56420.97 43 44 45 46 47 48 49 S 714397.57 880379.2 1084924.8 1336994.0 1647628.4 2030435.1 2502182.2 J 812279.54 1001002.9 1233573.9 1520179.9 1873375.5 2308631.7 2845014.5 V 3313280.28 4083081.1 5031735.8 6200799.1 7641480.1 9416886.1 11604786.2 G 114832.08 141511.9 174390.5 214908.1 264839.4 326371.6 402200.1 D 69529.71 85684.1 105591.8 130124.7 160357.7 197614.8 243528.3 $pop.sizes [1] 3.440000e+02 6.270000e+02 8.800097e+02 1.131070e+03 1.425927e+03 [6] 1.775273e+03 2.198519e+03 2.715653e+03 3.350341e+03 4.130956e+03 [11] 5.092034e+03 6.275872e+03 7.734447e+03 9.531719e+03 1.174646e+04 [16] 1.447570e+04 1.783901e+04 2.198372e+04 2.709139e+04 3.338576e+04 [21] 4.114254e+04 5.070152e+04 6.248141e+04 7.699821e+04 9.488782e+04 [26] 1.169339e+05 1.441020e+05 1.775824e+05 2.188416e+05 2.696868e+05 [31] 3.323452e+05 4.095616e+05 5.047184e+05 6.219836e+05 7.664940e+05 [36] 9.445797e+05 1.164041e+06 1.434492e+06 1.767779e+06 2.178502e+06 [41] 2.684650e+06 3.308397e+06 4.077063e+06 5.024319e+06 6.191659e+06 [46] 7.630217e+06 9.403006e+06 1.158768e+07 1.427994e+07 1.759771e+07 $pop.changes [1] 1.822674 1.403524 1.285293 1.260689 1.244995 1.238412 1.235219 1.233715 [9] 1.232996 1.232652 1.232488 1.232410 1.232372 1.232354 1.232346 1.232342 [17] 1.232340 1.232339 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 [25] 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 [33] 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 [41] 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 [49] 1.232338 2012-11-01 11:42:50.109 UTC long_term_proportional_stage_vector_plot1 Stage vector plot long term proportional: Is the proportion of individuals per stage in the long term (e.g.: 50 years) 2012-11-01 11:45:40.640 UTC damping_ratio1 2.0901. 2012-11-01 11:44:15.62 UTC The ratio between the dominant eigenvalue and the second highest eigenvalue of a transition matrix is called the damping ratio, and it can be considered as a measure of the intrinsic resilience of the population, describing how quickly transient dynamics decay following disturbance or perturbation regardless of population structure, (the larger the damping ratio, the quicker the population converges). High damping ratios tell you that the dominant stable stage distribution is reached fairly soon. 2013-08-01 14:19:45.876 UTC fundamental_matrix1 $N S J V G D S 1.00000000 0.0000000 0.0000000 0.0000000 0.0000000 J 0.05855658 1.0101010 0.0000000 0.0000000 0.0000000 V 6.89055876 11.9968091 13.3581662 10.0186246 12.9606017 G 0.20844800 0.4667882 0.3911175 2.9183381 0.6919771 D 0.12890879 0.2523299 0.2464183 0.1848138 1.2628940 $var S J V G D S 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 J 0.05631067 0.01020304 0.0000000 0.0000000 0.0000000 V 129.72009930 164.59050165 165.0824377 157.2694415 165.3219447 G 0.96474493 2.03981226 1.7387357 5.5983592 2.8680368 D 0.18007000 0.32133154 0.3152601 0.2478305 0.3320072 $cv S J V G D S 0.000000 NaN NaN NaN NaN J 4.052468 0.100000 NaN NaN NaN V 1.652910 1.069391 0.9618417 1.2517398 0.9920649 G 4.712035 3.059674 3.3713944 0.8107646 2.4473757 D 3.291836 2.246508 2.2785654 2.6936618 0.4562542 $meaneta S J V G D 8.286472 13.726028 13.995702 13.121777 14.915473 $vareta S J V G D 143.4207 181.1844 181.6537 177.2333 181.2319 2012-11-01 11:37:55.718 UTC Age specific survival The fundamental matrix workflow gives the basic information on age-specific survival, this includes the mean, variance and coefficient of variation (cv) of the time spent in each stage class and the mean and variance of the time to death. Fundamental matrix (N): is the mean of the time spent in each stage class. e.g.: For our Gentiana example means a J plants will spends, on average, about 1 year as a Juvenile, 11 as vegetative plant, less than a year a reproductive and dormant plant. Variance (var): is the variance in the amount of time spent in each stage class. Coefficient of variation (cv): is the coefficient of variation of the time spent in each class (sd/mean- the ratio of the standard deviation to the mean). Meaneta: is the mean of time to death, of life expectancy of each stage. e.g. The mean age at death is the life expectancy; the life expectancy of a new individual seedling is 8 years. Vareta: is the variance of time to death. 2012-11-01 10:47:32.328 UTC generation_time1 8.1302 2012-10-31 12:12:54.819 UTC Generation time: The time T required for the population to increase by a factor of Ro (net reproductive rate). e.g. for Generation time T and Net reproductive rate Ro: If T = 8.13 and Ro = 5.46 then the average individual in the study year replaced itself with about five new plants and took approximately 8 years to do so. 2012-11-01 12:15:30.218 UTC net_reproductive_rate1 5.4657 2012-10-31 12:13:22.678 UTC The net reproductive rate is the mean number of offspring by which a new-born individual will be replaced by the end of its life, and thus the rate by which the population increases from one generation to the next. 2012-11-01 11:40:25.562 UTC histogram_ci_95pc_of_lambda1 Histogram generated from the analysis of the confidence interval of lambda. 2013-08-01 14:17:03.163 UTC confidence_interval_95pc_of_lambda1 Calculate bootstrap distributions of population growth rates (lambda) by randomly sampling with replacement from a stage-fate data frame of observed transitions. Resampling transitions with equal probability. 2013-08-01 14:17:16.509 UTC survival_curve_plot1 A plot of survival curves is produced, one point for each stage. 2013-08-01 14:17:34.653 UTC sensitivity_matrix1 See Eigen analysis 2013-08-01 14:18:09.816 UTC sensitivity_plot1 A sensitivity matrix plot showing the sensitivities values of the actual transitions (i.e. the sensitivity values of non-zero elements) per stage. 2013-08-01 14:17:55.805 UTC elasticity_matrix1 See Eigen analysis 2013-08-01 14:18:26.84 UTC elasticity_plot1 An Elasticity matrix plot showing the elasticities values per stage. 2013-08-01 14:18:41.954 UTC cohen_cumulative_distance1 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 and returns a warning for imprimitive matrices (although will not function for imprimitive matrices with nonzero imaginary components in the dominant eigenpair) (Caswell 2001). 2013-08-01 14:18:53.434 UTC keyfitz_delta1 A distance measure between probability vectors of n (Observed Stage Distribution, abundances per stage) and w (Stable Stage Distribution). Its maximum value is 1 and its minimum is 0 when the vectors are identical. 2013-08-01 14:19:06.459 UTC Abundance_Interactionstages1abundances1initialYear0abundances11 Abundance iteraction: Initial abundance: In this dialogue authomatically appears the fields to fill out the initial abundance per stage observed in the field (see data below). After fill out the abundances, the user confirms the numbers. As a example Gentiana pneumonanthe has 5 stages with its respective abundance: stage abundance 1) S (seedlings) 69 2) J (Juveniles) 100 3) V (vegetative) 111 4) G (reproductive individuals) 21 5) D (dormant plants) 43 2012-11-01 11:56:16.812 UTC net.sf.taverna.t2.activitiesinteraction-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.interaction.InteractionActivity stages 1 text/plain java.lang.String false abundances 1 text/plain java.lang.String false initialYear 0 text/plain java.lang.String false abundances 1 1 http://biovel.googlecode.com/svn/tags/mpm-20130805/set_abundance.html LocallyPresentedHtml false net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 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net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeOutputFundamentalMatrixinput1output00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeOutputElasticityMatrixinput1output00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeOutputSensitivityMatrixinput1output00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeOutputEigenanalysisinput1output00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeOutputStageMatrixinput1output00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeOutputConfidenceIntervalinput1output00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeCalculateDefaultAbundanceyear0stages1data1abundances11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stages 1 false year 0 false data 1 false abundances 1 1 false localhost 6311 false false stages STRING_LIST year STRING data R_EXP abundances INTEGER_LIST net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeAbundance_InteractionstagesStageMatrixFromCensumatrixStagesAbundance_InteractionabundancesCalculateDefaultAbundanceabundancesAbundance_InteractioninitialYearStageMatrixFromCensuinitialYearsStageMatrixAnalysisabundancesAbundance_InteractionabundancesStageMatrixAnalysisiterationsiterationsStageMatrixAnalysislabellabelStageMatrixAnalysislongTermYearslongTermYearsStageMatrixAnalysisshortTermYearsshortTermYearsStageMatrixAnalysisstage_matrixStageMatrixFromCensustage_matrixStageMatrixAnalysisstagesStageMatrixFromCensumatrixStagesStageMatrixAnalysisreproductiveStageMatrixFromCensureproductiveStagesStageMatrixAnalysisrecruitedStageMatrixFromCensurecruitedStagesStageMatrixFromCensucensus_dataCensusData_ReadFromFilecensus_dataCensusData_ReadFromFilecensus_data_filecensusDataFileBootstrap_of__observrecruitedStagesStageMatrixFromCensurecruitedStagesBootstrap_of__observstartYearStageMatrixFromCensuinitialYearsBootstrap_of__observcensus_dataCensusData_ReadFromFilecensus_dataBootstrap_of__observstagesStageMatrixFromCensumatrixStagesBootstrap_of__observbootstrapIterationsiterationsBootstrap_of__observplotTitlelabelOutputFundamentalMatrixinputStageMatrixAnalysisfundamentalMatrixOutputElasticityMatrixinputStageMatrixAnalysiselasticity_matrixOutputSensitivityMatrixinputStageMatrixAnalysissensitivity_matrixOutputEigenanalysisinputStageMatrixAnalysiseigenanalysisOutputStageMatrixinputStageMatrixFromCensustage_matrixOutputConfidenceIntervalinputBootstrap_of__observconfidence_intervalCalculateDefaultAbundanceyearStageMatrixFromCensuinitialYearsCalculateDefaultAbundancestagesStageMatrixFromCensumatrixStagesCalculateDefaultAbundancedataCensusData_ReadFromFilecensus_datastable_stage_distributionStageMatrixAnalysisbarPlotprojection_matrixStageMatrixAnalysisprojectionMatrixeigenanalysis_elasticity_matrixStageMatrixAnalysiseigenanalysis_elasticity_matrixeigenanalysis_sensitivity_matrix_1StageMatrixAnalysiseigenanalysis_sensitivity_matrix_1eigenanalysis_sensitivity_matrix_2StageMatrixAnalysiseigenanalysis_sensitivity_matrix_2stage_matrixOutputStageMatrixoutputeigenanalysisOutputEigenanalysisoutputshort_term_stage_vector_plotStageMatrixAnalysisstageVectorPlotShortTermlong_term_logarithmic_stage_vector_plotStageMatrixAnalysisstageVectorPlotLongTermLogarithmicpopulation_projectionStageMatrixAnalysispopulationProjectionlong_term_proportional_stage_vector_plotStageMatrixAnalysisstageVectorPlotLongTermProportionaldamping_ratioStageMatrixAnalysisdampingRatiofundamental_matrixOutputFundamentalMatrixoutputgeneration_timeStageMatrixAnalysisgenerationTimenet_reproductive_rateStageMatrixAnalysisnetReproductiveRatehistogram_ci_95pc_of_lambdaBootstrap_of__observhistogramconfidence_interval_95pc_of_lambdaOutputConfidenceIntervaloutputsurvival_curve_plotStageMatrixAnalysissurvivalCurvePlotsensitivity_matrixOutputSensitivityMatrixoutputsensitivity_plotStageMatrixAnalysissensitivityPlotelasticity_matrixOutputElasticityMatrixoutputelasticity_plotStageMatrixAnalysisElasticityPlotcohen_cumulative_distanceStageMatrixAnalysiscohenCumulativekeyfitz_deltaStageMatrixAnalysiskeyfitzDelta 74f6db81-5630-4171-abfe-07de7363f059 2012-10-29 13:43:44.823 UTC 199c86f0-df6a-425c-b4dd-6aa852bcbff5 2012-07-13 09:03:22.271 UTC e9fc8f5f-13bf-432d-875e-3c5858b128d0 2012-12-10 16:26:39.762 UTC 42e22b26-02a1-413f-99ef-dfa978e86c1f 2012-10-23 14:48:02.159 UTC 745cb63a-e27a-4cbf-8fc0-5a1be24ed0ac 2012-11-01 10:17:46.562 UTC 53b6584e-7b75-48ac-976b-11154cef701f 2012-07-13 09:04:05.440 UTC e05732a8-9627-4bfa-b871-d8d4a9c53fb6 2012-07-11 20:55:29.517 UTC 4753d12c-1f1d-4454-97fe-6a2dcecc38b6 2012-07-13 07:45:12.849 UTC ec826e1b-1a3a-4e34-b4a8-1c0d4b96d68d 2013-06-21 09:30:33.650 UTC d98aaf13-21d0-487f-bb75-eec4eaf390a7 2012-10-29 13:45:38.787 UTC 87f752c3-6599-4607-8858-76f668ae65d2 2012-11-01 10:43:10.78 UTC f7125a55-d200-4c9f-9806-6b05549b428a 2012-10-29 13:42:50.131 UTC 9c7eab3e-170b-4f26-bb3e-bdc22eecec62 2012-11-01 11:51:49.687 UTC 36fe5bbb-4c71-45c0-b68d-472ccadbcaba 2012-11-01 10:14:45.687 UTC ecaea223-12cf-4139-8d0e-03f7ee9539d0 2012-10-17 15:30:24.563 UTC 543f4718-3a39-4813-b1f8-b8173435abc1 2012-10-10 15:38:22.120 UTC ac715f0e-f99c-46df-bfe6-0c70a550bb61 2012-11-01 11:48:54.375 UTC 2615d90a-7a5e-464e-89b6-20fec7bc1014 2012-07-13 09:11:19.100 UTC a6f209c5-1062-4064-b41a-91caff11da78 2013-06-10 15:21:06.432 UTC ff0b6521-d86f-4244-ad22-561b6b52803c 2013-07-30 05:49:06.361 UTC 95e5f4d5-c433-4be8-852f-c338eac52049 2012-10-08 10:37:08.148 UTC 11db6475-ebfc-492f-af7d-3a4185d279ce 2012-09-19 15:49:14.786 UTC 3618d570-4189-464f-8921-ae27391d6512 2012-10-18 11:17:27.472 UTC 78c30687-4ce1-405e-a822-f624fef969b9 2012-10-30 10:40:44.746 UTC e9b5e5d2-9871-4eba-b7a1-0635c8fc1bf5 2012-07-13 09:10:07.45 UTC 6c9032a6-6f3a-43fc-85f7-4a65f24bdfa0 2012-10-30 11:05:28.590 UTC a23877ec-cc0a-43f7-958e-eb68ab303cb8 2013-06-10 15:15:29.501 UTC 4b6fd6d1-6c87-4a63-be07-dc60194412cf 2013-07-17 09:54:44.455 UTC 9e143465-5ee4-45f6-9c66-8d740fe4cd4c 2012-10-30 09:57:13.720 UTC 2e971f14-ccf6-4de8-aa61-0a1282f7cb69 2013-08-01 14:20:09.608 UTC e0424e01-ed30-4c94-95b8-143751fca7a2 2012-07-11 16:07:29.317 UTC c2d28536-2e5b-45f0-aa16-8a7e15d668de 2012-10-23 14:23:17.957 UTC bd483775-9c66-42a8-bf44-153f086ae7ba 2012-10-26 10:38:50.743 UTC 5050c951-86f4-40c4-a3d5-5efc37dea1c6 2013-06-21 09:25:55.888 UTC f10f5937-62eb-44eb-bc4d-44e2fd2aa3cc 2013-06-24 10:18:11.21 UTC 2de604ef-97f6-4f76-9392-556104352c06 2012-11-26 15:47:00.314 UTC d4201bf9-75b9-40af-9d80-c2ea6a019c66 2012-10-30 07:09:00.183 UTC 5639ee4e-2035-4cc9-911f-fb435d58a930 2012-10-07 22:14:42.810 UTC 6addddd1-bdf4-4f01-a226-aed869898b46 2012-10-23 15:39:43.253 UTC 12eeeead-ffb3-4fcd-944c-efcb7de6a5fd 2012-10-18 10:18:38.541 UTC Maria Paula Balcázar-Vargas, Jonathan Giddy and Gerard Oostermeijer 2012-10-30 15:58:04.755 UTC abbcd7d0-d152-4988-a1fc-d5149f19410c 2012-11-01 10:49:50.46 UTC 9fb897f9-900c-44e1-9e2a-c77b3e4fb7bc 2013-08-01 13:57:11.549 UTC e8d180a6-7a5c-4421-b788-db26f3589ae8 2012-09-20 13:43:51.734 UTC 28cbe7df-43ca-4479-a886-649875ad33df 2012-10-30 15:59:46.983 UTC fbfe4abd-96b0-4223-94c1-eba2a2f968fb 2012-10-23 09:36:25.955 UTC b546696d-5a8f-453b-9126-610a834a496c 2013-08-06 19:47:31.270 UTC f0b240a1-0585-4dd9-937b-560ed8f60bad 2012-10-26 11:24:13.754 UTC aab9fd22-7f16-41d7-a280-1f426ec85097 2012-10-23 15:42:20.102 UTC 7ee877e3-c858-4eef-a9ea-44411f5ccc36 2013-06-24 09:54:45.584 UTC d64b5ae1-a5ad-4260-89b2-9822403cfc28 2012-10-17 14:53:12.993 UTC d63e1001-8c5e-4619-88e8-829e59b157c4 2012-09-28 08:44:45.610 UTC c8f2d1e0-de4e-44c9-a758-efca43706d64 2012-11-01 10:26:06.156 UTC 18e99b4e-e6d1-4c87-86cb-f4b3f40fec87 2012-07-13 08:43:47.651 UTC dbb35d79-9de3-4274-8c9a-da1dc407203d 2013-08-06 19:44:18.584 UTC a422750b-03d7-4445-8898-a7c7099fd68e 2013-07-30 05:45:19.469 UTC c514cd8f-bb14-4ade-9559-70630a8a1cd2 2013-08-06 15:40:10.732 UTC 92f46bea-cd6c-467d-b850-acfaf4f96581 2012-10-17 15:47:45.898 UTC 3652cbd1-72c4-47e1-9479-ddedb5f81d98 2012-07-13 09:07:01.485 UTC ff5bc61b-0d13-4622-adc8-2aedd7b7038c 2012-07-13 09:20:52.134 UTC 36eb1c8d-597a-4ca0-9670-b88b8b203d95 2013-06-21 13:58:06.556 UTC 31de0f25-8b52-4b7c-a868-4eb26381b768 2012-07-11 16:08:08.989 UTC 92219602-e4d9-4381-a61c-1956a05664d4 2013-06-21 09:23:14.7 UTC 9fa97e87-bd43-4312-a36f-8ac512988b80 2012-07-13 06:25:39.0 UTC acb06188-582e-4ece-91ab-034e55a57158 2012-10-23 15:39:06.238 UTC b740ea7f-7c1a-4ae3-b632-e22a0879bd5c 2012-10-12 09:21:33.957 UTC 65f8398f-84f2-452c-be80-fc40ca35bfcc 2013-07-17 10:04:40.405 UTC 1144714e-3e7e-46d7-817a-80f25646e7e0 2012-10-17 12:17:52.970 UTC 480fcb53-df8f-4656-b52b-a1b8dd3d850d 2012-10-23 15:04:23.405 UTC 5fcf9a43-b268-404c-847d-0ca38ece3ffd 2013-06-10 15:46:46.687 UTC 6a6df203-47b2-4b26-855f-ff8c48eed2bc 2012-09-19 15:38:42.701 UTC 59c1f00e-85e1-4548-a1e9-32fe56dc0de5 2012-10-16 15:21:54.632 UTC 0b9f50d2-2082-445a-9321-26618c1783b1 2013-06-21 10:54:15.434 UTC 694e1ed0-cb74-49f8-9f6e-0260408c0121 2013-08-05 16:12:36.120 UTC cc5b393c-818f-4016-a1d8-890885b584d3 2012-07-13 09:00:07.232 UTC bf8915cd-faa0-49cb-a181-192540beb0b7 2014-07-03 14:14:22.383 UTC 290bff82-b044-4475-9f8a-65e5a8ee3c3b 2012-11-01 11:51:22.265 UTC 1426c3c1-f38e-452e-8b2a-d045c56296e1 2012-10-16 15:15:35.841 UTC a6efda97-4c1a-4ce4-9b65-6a87f8d11e78 2012-07-13 09:14:16.348 UTC a0cb373d-9230-4224-8450-1128a876725a 2012-10-31 12:11:53.522 UTC 5bdb4704-9322-4cbc-8d08-4b87aa1f2318 2012-07-11 22:49:36.115 UTC 903d6aaa-d52b-44cd-a3d3-04b79fc38768 2013-08-01 14:16:23.460 UTC ace5fe46-77ef-4a86-b67b-bde22c741ac8 2012-07-13 09:26:09.444 UTC 9bd50d2a-92c9-4ca0-a18d-f93ad67d40aa 2013-06-24 10:14:48.529 UTC 9c9a25e0-f9f5-4a03-b681-b41060114a60 2012-11-26 15:43:26.632 UTC a02d437d-4791-479f-9609-dfc1bc8adcfd 2012-11-21 15:25:22.331 UTC bef35e44-1127-458d-b2ee-61048bd64a07 2012-07-13 06:22:03.734 UTC 41d22268-b28d-41d6-a62a-2bd6c9588ef8 2012-11-01 11:39:57.328 UTC f8683e08-b7e0-4a1b-a119-e977d6ba23e5 2012-12-09 23:15:12.16 UTC f6d04a4d-5c51-4197-9065-e44dceeac09d 2013-06-21 13:43:11.679 UTC 809f0035-d1b9-448d-bae6-6481cc68b7c7 2012-10-29 13:11:22.192 UTC 69d11822-5bf3-40d1-a6ce-cc0110a366dd 2012-07-13 08:06:33.163 UTC 3f094d6e-7d4f-43ea-a672-8f56b7e9b327 2012-09-19 15:54:02.713 UTC 3e775ac4-0bf5-435e-979e-26753958554c 2012-07-13 06:22:29.620 UTC e491d0ce-ea60-47c4-94f0-cb8f923433da 2012-10-23 15:41:09.199 UTC 575ff213-a073-4737-9a89-9ee7d0bd18ae 2013-06-24 10:20:52.350 UTC 0524104c-efee-44ad-a088-f428caefeb8d 2012-10-30 10:41:29.116 UTC 485eeece-ff3e-4ab6-92ac-8a707279130b 2012-11-26 16:08:28.274 UTC b43beb73-dc0f-46e8-955e-a812ec1eb70f 2012-11-21 15:48:35.252 UTC e8da845b-093d-4f3c-9e1c-8dbcfdb703fb 2012-10-17 15:44:05.374 UTC 34d1cf2c-a02a-4b6e-b39d-b6f55da1a025 2012-07-11 20:58:04.370 UTC d2e98a62-d554-4654-a359-39eaa6c9c9d5 2012-09-20 13:35:32.442 UTC ca39ee21-5bd7-4068-8fcf-f69199409104 2012-10-23 15:44:47.549 UTC 1fe1ceb9-0ce6-4fc7-b2d3-693545144393 2012-11-21 15:37:01.459 UTC 61e17b4e-2110-470a-8aa8-db97ea3341e0 2012-07-11 15:46:57.885 UTC c6a90652-02c6-4d82-939c-c6ece81e41dc 2013-08-01 14:14:34.911 UTC cfc0e9b8-dfd4-48f7-8f9f-2c9c121fda8f 2012-11-21 15:32:07.803 UTC a446c0ff-a592-4568-b9d9-92c56d0ad1d1 2013-08-01 13:56:15.513 UTC 9f3c6f78-3407-4093-9c43-7b3f2bbdf1ff 2013-06-21 11:04:07.52 UTC 8b100d25-27a9-4e50-893b-27ca7f42a4e2 2012-10-23 12:02:36.167 UTC 9cc9920c-7d50-4c5b-9800-20cc1cfd72cf 2012-09-20 14:54:19.489 UTC 6315c9ba-381a-44ce-82cd-ea6f5aae35d5 2012-07-13 09:27:13.462 UTC Matrix Population Model construction and analysis v20 2014-07-03 14:13:26.890 UTC 36a6e99d-5c0a-4bf1-999a-9f15e62ad239 2013-06-21 13:39:43.785 UTC e8d2435e-c0dc-4d7a-86a6-3aaa6fbf5b60 2012-10-08 10:18:15.958 UTC 85c44158-b0f7-4e74-a6d7-b2d0b0093cc2 2012-10-31 12:14:30.69 UTC The Matrix Population Models Workflow provides an environment to create stage-matrices with no density dependence and to perform several analyses on them: - Eigen analysis; - Age specific survival; - Generation time (T); - Net reproductive rate (Ro); - Transient Dynamics; - Bootstrap of observed census transitions (Confidence intervals of lambda); - Survival curve; - Keyfitz delta; - Cohen's cumulative distance. If multiple year transitions are selected, the results will be ordered by year. This workflow requires an instance of Rserve on localhost 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 uses R packages ‘popbio’ (Stubben & Milligan 2007; Stubben, Milligan & Nantel 2011) and 'popdemo' (Stott, Hodgson and Townley 2013). References: Caswell, H. 1986. Life cycle models for plants. Lectures on Mathematics in the Life Sciences 18: 171-233. Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts. de Kroon, H. J., A. Plaiser, J. van Groenendael, and H. Caswell. 1986. Elasticity: The relative contribution of demographic parameters to population growth rate. Ecology 67: 1427-1431. Horvitz, C., D.W. Schemske, and Hal Caswell. 1997. The relative "importance" of life-history stages to population growth: Prospective and retrospective analyses. In S. Tuljapurkar and H. Caswell. Structured population models in terrestrial and freshwater systems. Chapman and Hall, New York. Jongejans E. & H. de Kroon. 2012. Matrix models. Chapter in Encyclopaedia of Theoretical Ecology (eds. Hastings A & Gross L) University of California, p415-423 Mesterton-Gibbons, M. 1993. Why demographic elasticities sum to one: A postscript to de Kroon et al. Ecology 74: 2467-2468. 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., S. Townley and D.J. Hodgson 2011. A framework for studying transient dynamics of population projection matrix models. Ecology Letters 14: 959–970 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 van Groenendael, J., H. de Kroon, S. Kalisz, and S. Tuljapurkar. 1994. Loop analysis: Evaluating life history pathways in population projection matrices. Ecology 75: 2410-2415. 2013-08-06 19:47:29.153 UTC 78851c10-ff00-4598-b0e3-e0e96ae2ddb2 2012-11-01 11:44:23.218 UTC 67747550-2202-4501-9737-d970ee30bbf3 2012-10-31 12:09:49.944 UTC 9911f697-7c3a-4c84-9f68-dcd18fa5f907 2012-10-10 16:16:36.407 UTC 51e48dec-1225-494a-a91d-3e17eb05f5c9 2012-11-26 15:38:34.136 UTC d7781dea-d32c-4a00-a1b2-d67f151c32c2 2012-10-17 15:49:32.177 UTC 1378f96b-c536-4968-a27f-2c7956207e6f 2012-10-12 09:24:28.167 UTC 3b844874-3fc8-4b62-9f02-5c86ae0e053d 2012-07-13 09:02:25.256 UTC 6e4e9dd0-334b-403a-9632-8423f9636fe7 2012-07-11 13:00:55.837 UTC ffaaa352-51bf-4d02-83af-18512fb8b076 2013-06-10 15:07:19.413 UTC bf460b28-85d7-4bbd-bd14-a89dabcc1eb7 2012-11-01 11:56:17.546 UTC a580ca8f-7e5c-4826-a719-5160f00fc4b8 2013-06-21 10:54:48.653 UTC 3e559db0-7ac1-47cf-b89a-aa9cbf3dd135 2012-10-30 09:34:50.603 UTC c878bbfb-70a8-4f5e-bd9f-26c8987fc506 2013-08-06 19:48:05.297 UTC 734e5b89-dbe5-4ffc-8983-e3a2d1b8edc2 2012-11-01 10:47:38.765 UTC 9159d5c8-8084-403e-9941-37a0babea12d 2012-07-13 08:10:15.746 UTC 6bf5fc1d-7f9a-48a3-8b77-b4cf27f2b52a 2012-10-17 14:47:18.255 UTC 7e7def24-2171-4f57-ab38-22e7f0b9e1ec 2013-07-29 17:05:19.693 UTC 2e59c48d-737b-4181-94aa-7d2643b8387a 2013-06-21 11:09:17.93 UTC 27fa10ea-ca41-4311-b50c-eb7e2fe66489 2012-07-11 15:49:21.753 UTC 6933ad38-f960-4f05-ae40-0352c2a97723 2012-07-11 16:13:31.963 UTC c1878ed1-4b8e-4a7c-ba49-79e0aa4d6b73 2012-12-10 16:19:50.245 UTC 601f3baa-7b7c-4531-a7ba-bec931308123 2014-07-03 14:13:28.906 UTC 21662d80-f207-4a54-9844-8f24acb91a27 2012-07-13 09:22:22.131 UTC 2d852a79-7b62-40cc-a176-aadadbfc2c03 2012-10-17 14:40:53.717 UTC 16d6c6d0-fab4-4be8-a448-fa24c023aac0 2013-06-21 13:50:13.402 UTC 96bcf3e2-a8dd-48ed-9160-d9416b093a83 2012-11-01 14:54:16.704 UTC b0e4235a-5fe4-4228-a361-9dd543d8ec95 2012-09-19 15:33:22.278 UTC 13d232ff-44be-41d4-8e07-aa3a351073f0 2013-06-10 15:19:11.973 UTC 477d4f86-74ce-422e-bfb7-6a1057a43f3d 2012-10-23 16:07:32.793 UTC da20ba77-b976-435f-a0f7-fe68543421d0 2013-06-24 09:59:18.267 UTC 366c8de1-e00d-447a-b851-7d7dc5deef4e 2012-10-17 16:05:49.72 UTC a8004bfd-bcb7-4f8f-986c-a4c03dc24ca7 2013-02-12 09:16:55.687 UTC f9d69679-5e04-46e4-b77b-c70d19cb4c0d 2012-07-13 08:51:15.886 UTC 57430714-9ca4-411f-a4d2-896d3bd1eabf 2013-08-01 14:14:21.190 UTC a2cee785-18ef-4208-8677-b37970b920a7 2012-07-13 07:51:10.754 UTC 4988e019-bf93-40bd-b095-6a33aec7e968 2012-07-13 08:39:49.679 UTC 44f38a9d-ca0e-4648-becd-807d0b736f04 2012-10-17 15:28:51.127 UTC 104a6f4f-33c3-4b8c-b4d7-55ac8fa330f7 2012-07-13 07:53:21.635 UTC 6317b32f-0f55-4e22-9c4b-72bbe80796ca 2012-10-17 11:48:27.846 UTC ada91081-1164-46f7-9ae2-1b1a9492fc4a 2013-06-13 10:54:26.885 UTC ecef24cd-4e05-4e89-b47d-2627857c98bc 2012-07-13 09:06:31.770 UTC 09aa9ebd-4e1d-4579-83f6-727da90eae35 2012-09-20 13:38:43.850 UTC dc590143-bfad-4167-b27c-b57aa45a0d96 2012-11-01 12:16:36.328 UTC 0c6d94db-6fd7-440a-a306-99b63cd378e7 2012-10-20 06:48:08.975 UTC d876c281-ea57-4da0-a470-f3bfbd4c7ad4 2012-07-11 13:05:24.619 UTC ac5a0e53-1ce5-48c4-aa7f-857d0185c16f 2012-10-26 10:36:36.257 UTC 214b1114-efbc-4d54-bb20-4fa1f9beb89f 2012-09-20 13:37:17.940 UTC ba0a12e6-e2cc-428f-b901-978b4d2e8320 2012-10-18 11:26:48.0 UTC 476bc827-2680-4ba5-b6b6-878b59202d8f 2012-10-30 09:50:32.390 UTC 21f58597-45eb-4183-bcbb-fe43ada440b2 2013-06-21 10:04:50.884 UTC Workflow188input11output0PrettyPrintRinput1output00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.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-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokePrettyPrintRinputinputoutputPrettyPrintRoutput 1c210d9a-059f-4c52-8df7-0310db5e211f 2013-06-21 14:30:38.141 UTC deb0863c-b567-4755-af9c-b8b74d5e08e5 2013-06-21 14:31:25.529 UTC SortAndCategoriseStacensus_data11stageColumnHeader00matrixStages1recruitedStages1reproductiveStages1ParseStages_FromCensusDatastageColumnHeader0census_data1allStages11net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeCategoriseStages_InteractionunsortedStages1recruitedStages11reproductiveStages11sortedStages11 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-activity1.5-SNAPSHOTnet.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/tags/mpm-20130805/select_stages.html LocallyPresentedHtml false net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeParseStages_FromCensusDatastageColumnHeaderstageColumnHeaderParseStages_FromCensusDatacensus_datacensus_dataCategoriseStages_InteractionunsortedStagesParseStages_FromCensusDataallStagesmatrixStagesCategoriseStages_InteractionsortedStagesrecruitedStagesCategoriseStages_InteractionrecruitedStagesreproductiveStagesCategoriseStages_InteractionreproductiveStages f0be6e07-8fa8-4d7c-b86a-438ca0446799 2012-07-11 13:20:57.597 UTC f421171e-a6bf-43d7-86b2-698255bd39f3 2012-07-06 07:07:32.666 UTC f787870d-2e46-4690-ae74-fbbf081fadf8 2012-07-13 06:21:53.243 UTC 726ce44a-a330-4362-887d-0b718fca8485 2012-10-17 11:06:22.517 UTC 1c93c7b5-4270-48db-9083-a6a7ff1f4887 2012-07-06 07:00:03.815 UTC 592b9f2e-fcf1-49f5-952b-faa461b53ba7 2012-10-17 11:27:59.579 UTC bcfcd2c0-adc4-4ac2-82c1-1be7ef7099fb 2012-10-17 16:04:52.79 UTC 20a399cf-7784-47b3-a40d-93d618f79137 2012-07-11 14:15:34.830 UTC d5ce2f50-4e3f-41a9-8860-bc701d7681a4 2012-10-17 11:06:54.436 UTC dc6206c9-7fe9-4d01-aabd-467efd437717 2012-07-13 06:19:12.877 UTC 7635bc83-f341-40b2-82aa-9e8f960a8e2b 2012-07-11 12:36:11.769 UTC 9ee8406f-b625-4130-a04c-7ef1721c8572 2012-10-17 11:16:07.104 UTC 87e7040d-54ea-4f8b-8638-2945c37877a1 2012-10-07 22:14:35.518 UTC 81624fcb-191c-43b4-8009-7007913e2603 2012-07-11 14:08:48.463 UTC 1e6b402b-d71a-4e09-89c8-529b7a4c6ca0 2012-07-11 14:12:08.550 UTC 4ff7f14e-1340-42b8-aee8-5c8bd1bb34c2 2012-07-06 07:00:42.164 UTC 61b01f4b-0f97-42d1-ad6b-526d7111a154 2012-07-06 07:02:27.552 UTC SortAndCategoriseStages 2012-10-17 11:05:25.583 UTC 4b28bd15-1c07-4b9b-a98a-b7a0e2fa1f80 2012-07-06 06:58:00.480 UTC e9ca405a-3721-4f23-969d-25eb21f17b76 2012-11-01 14:53:37.2 UTC e5cadf2e-40f8-473e-898d-a5fd35f0a426 2012-07-13 06:19:48.93 UTC d9512ecc-29c2-4cb3-80e8-448c80c6bc31 2012-07-11 14:15:54.232 UTC Net_reproductive_ratstage_matrix11 The stage matrix without the stage names (as you see in the example). 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. 2012-10-30 10:56:07.8 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 2012-10-05 11:35:18.26 UTC rows_fecundity11 To perform the Net reproductive rate (Ro), the row(s) in which the recruitment values are found, should be selected. In the example of the Gentiana species (Oostermeijer et al. 1996. The Journal of Ecology): Selected lines are S and J (seedlings and juveniles, these stages receive recruits each year from the stage G): therefore, the numbers 1 and 2 are used to identify these rows (S and J). 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 2012-10-11 12:38:55.125 UTC 1 2 2012-10-11 12:07:35.828 UTC columns_fecundity11 To perform the net reproductive rate (Ro), the column(s) in which the fecundity values are found, should be selected. In the example of the Gentiana species (Oostermeijer et al. 1996. The Journal of Ecology): The selected column is G (reproductive individuals): therefore number 4 will be used to identify the fecundity column (G). 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 2012-10-11 12:41:42.156 UTC 4 2012-10-11 12:41:11.468 UTC netReproductiveRate0 5.568522 2012-10-11 12:29:27.375 UTC The net reproductive rate (Ro) is mean number of offspring by which a newborn individual will be replaced by the end of its life, and thus the rate by which the population increases from one generation to the next (Caswell, 2001) 2012-10-05 13:15:39.386 UTC NetReproductiveRateAnalysiscolumns1rows1stage_matrix1result00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false rows 1 false columns 1 false result 0 0 false localhost 6311 false false stage_matrix R_EXP rows DOUBLE_LIST columns DOUBLE_LIST result DOUBLE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeNetReproductiveRateAnalysiscolumnscolumns_fecundityNetReproductiveRateAnalysisrowsrows_fecundityNetReproductiveRateAnalysisstage_matrixstage_matrixnetReproductiveRateNetReproductiveRateAnalysisresult 24c4057c-5489-458e-a3f7-fbf879964592 2012-10-11 12:39:01.78 UTC c55402cf-b409-47b3-b079-4cfb1234f6ce 2012-10-05 11:30:55.729 UTC 6cefb97d-5207-40ec-847d-6b62cabd12cb 2012-06-15 06:56:38.363 UTC f92df37a-086b-4bbe-b7c2-42bc8660915e 2012-06-15 06:42:22.376 UTC 6e3a57e1-23a5-4883-84d6-bf7a14a9b104 2012-06-21 14:06:12.125 UTC b7b02776-ce57-4b3f-bc6e-b2991aaa4bc7 2012-10-11 12:41:42.343 UTC 81787775-16fe-4c13-90dd-0a48678146ee 2012-09-28 08:10:30.76 UTC 7fc87bf9-2dd0-4cc3-8fe6-7ea750b7ef4a 2012-10-05 12:54:17.151 UTC fbc0d966-2c11-49ab-be84-561bb64881ad 2012-10-30 10:55:34.10 UTC 7aff96c5-929a-4be8-a552-37f0bbfb3137 2012-10-30 10:56:15.506 UTC c4dc3143-373c-4d4e-adc3-8d8d84dd4a1e 2012-10-05 11:50:33.307 UTC f589ea15-b89d-415f-b16b-71d6b6abcc59 2012-09-28 08:41:41.388 UTC 138bd86c-fd0b-4db2-8092-1e9071940e2a 2012-09-28 08:45:04.951 UTC 1ea1c8b7-0fbe-4b02-8a42-f01ed9ef0f98 2012-10-11 12:29:40.843 UTC c65932e5-9f7b-4ec2-b218-152de68c3263 2012-10-05 11:48:11.370 UTC 3b27b30c-4431-409c-97fd-26b37a8823fb 2012-09-28 08:09:20.60 UTC 74dedf92-5055-45bd-995b-5b9df0a480a8 2012-10-05 11:40:27.651 UTC Net reproductive rate (Ro) 2012-10-05 13:06:49.104 UTC b5ec6e6b-d9bd-43b3-86d4-f33bd5640243 2012-10-05 11:37:41.839 UTC 98ee462f-3718-4938-a959-6a042ad29aff 2012-10-05 11:35:18.229 UTC ab28f080-c1c8-43fe-a71e-a83179807fe0 2012-10-05 13:16:14.823 UTC c7b6d5dd-ddc3-4388-9f28-647b74d4f5f4 2012-06-15 06:54:00.692 UTC ce28076c-2872-4c26-aa87-5c8a90bde441 2012-10-05 13:16:51.604 UTC 1513fefa-e519-421a-9766-7da5cbcc12ac 2012-10-30 10:54:39.215 UTC e8363f39-9c04-411e-8d92-7582d6f97c84 2012-10-05 13:07:24.964 UTC 19bbf243-d943-409c-9004-bb9df6c33349 2012-06-15 06:40:07.359 UTC 227252e7-ff41-47fd-af0c-39907febdc42 2012-09-28 08:07:38.560 UTC The net reproductive rate (Ro) is mean number of offspring by which a newborn individual will be replaced by the end of its life, and thus the rate by which the population increases from one generation to the next (Caswell, 2001) 2012-10-05 13:09:39.573 UTC 18aac4ef-4208-458c-9dbb-9a2e5ee2f3f9 2012-06-21 14:24:41.784 UTC c4bce93c-e05f-433c-9f0f-36cb69f249e7 2012-10-05 11:49:57.167 UTC a514d7f6-ae3a-46d8-ac68-684b622d5b54 2012-10-11 12:06:15.687 UTC 45f7861f-9c3c-492a-924c-34b896fefa32 2012-10-05 11:35:49.386 UTC 3979d4bd-1668-4e90-913b-06b22e1229c6 2012-06-22 08:39:22.770 UTC e2a9d92c-412f-47fc-a567-39a56abe723e 2012-06-15 06:52:47.83 UTC aabd83c8-3016-47ec-a1e2-e6d909289dfa 2012-06-22 08:38:17.934 UTC 5e006443-a6ca-46e2-85ec-b5fe88262055 2012-10-05 13:09:39.807 UTC Maria Paula Balcázar-Vargas, Jonathan Giddy and Gerard Oostermeijer 2012-10-05 11:21:40.276 UTC a6d860df-36bb-423a-a7e6-586818fccfc8 2012-09-28 08:06:19.732 UTC c6fd9af5-b7a7-460d-83c8-8122aa11253e 2012-09-28 08:43:02.498 UTC 724a9578-a6b6-44b9-9c02-fcf83e375a67 2012-09-28 08:40:05.123 UTC e6545437-85f0-42a0-b54f-066b165d7ec6 2012-10-05 11:51:02.854 UTC Keyfitz_Deltaabundances11stage_matrix11keyfitzDelta0CalculateKeyfitzDeltaabundances1stage_matrix1keyfitz_delta00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false abundances 1 false keyfitz_delta 0 0 false localhost 6311 false false stage_matrix R_EXP abundances INTEGER_LIST keyfitz_delta DOUBLE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeCalculateKeyfitzDeltaabundancesabundancesCalculateKeyfitzDeltastage_matrixstage_matrixkeyfitzDeltaCalculateKeyfitzDeltakeyfitz_delta Keyfitz Delta 2013-01-22 10:43:22.424 UTC 4403aaae-f67d-4cde-8d3e-6f8f9c817f65 2013-01-22 06:45:32.736 UTC e420b581-0054-4d82-af0e-79182396dcc6 2013-01-22 06:45:58.176 UTC 350ad4f3-8adb-42a8-bfd6-b9a67cdb739d 2013-01-22 07:43:36.673 UTC 2bbadcb3-679a-4798-83a7-1fd99ae7d898 2013-01-22 10:45:01.682 UTC Generation_time__T_stage_matrix11 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 2012-10-05 11:35:18.26 UTC A stage matrix In this import port a stage-matrix should be add. The input data (a .txt-file) has to be a point delimited (see example). In the pop-up ‘run workflow’ window, click-on ‘set a location’ and seach in your computer the txt-file of your matrix. 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. 2012-10-11 11:35:59.0 UTC rows_fecundity11 1 2 2012-10-11 11:53:42.500 UTC To perform the generation time (T), the row(s) the row(s) in which the recruitment values are found, should be selected. In the example of the Gentiana species (Oostermeijer et al. 1996. The Journal of Ecology): Selected lines are S and J (seedlings and juveniles, these stages receive recruits each year from the stage G): therefore, the numbers 1 and 2 are used to identify these rows (S and J). 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 2012-10-11 12:42:20.515 UTC columns_fecundity11 4 2012-10-11 11:58:22.562 UTC To perform the generation time (T), the column(s) in which the fecundity values are found, should be selected. In the example of the Gentiana species (Oostermeijer et al. 1996. The Journal of Ecology): The selected column is G (reproductive individuals): therefore number 4 will be used to identify the fecundity column (G). 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 2012-10-11 12:42:12.468 UTC generation_time0 8.055 2012-10-17 13:36:56.312 UTC Generation time (T): The time T required for the population to increase by a factor of Ro (net reproductive rate) e.g.: If Ro (net reproductive rate) = 5.56 and T (generation time) = 8.055 The average plant of the species Gentiana pneumonanthe in Terschelling in the year 1987 replaced itself with almost six new plants and took approximately 8.05 years to do so. 2012-10-11 12:03:38.359 UTC GenerationTimeAnalysiscolumns1rows1stage_matrix1result00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false rows 1 false columns 1 false result 0 0 false localhost 6311 false false stage_matrix R_EXP rows DOUBLE_LIST columns DOUBLE_LIST result DOUBLE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeGenerationTimeAnalysiscolumnscolumns_fecundityGenerationTimeAnalysisrowsrows_fecundityGenerationTimeAnalysisstage_matrixstage_matrixgeneration_timeGenerationTimeAnalysisresult c65932e5-9f7b-4ec2-b218-152de68c3263 2012-10-05 11:48:11.370 UTC 3979d4bd-1668-4e90-913b-06b22e1229c6 2012-06-22 08:39:22.770 UTC c55402cf-b409-47b3-b079-4cfb1234f6ce 2012-10-05 11:30:55.729 UTC 3b27b30c-4431-409c-97fd-26b37a8823fb 2012-09-28 08:09:20.60 UTC e2a9d92c-412f-47fc-a567-39a56abe723e 2012-06-15 06:52:47.83 UTC 45f7861f-9c3c-492a-924c-34b896fefa32 2012-10-05 11:35:49.386 UTC 88440626-1691-4815-909b-c08836952d2a 2012-10-11 11:45:15.453 UTC d9758ade-7727-44a4-aa75-d0fac485df0d 2012-10-17 13:37:14.171 UTC 98ee462f-3718-4938-a959-6a042ad29aff 2012-10-05 11:35:18.229 UTC 96405b62-f038-4c52-a831-1787aa510bf0 2012-10-11 12:40:08.593 UTC 81787775-16fe-4c13-90dd-0a48678146ee 2012-09-28 08:10:30.76 UTC 227252e7-ff41-47fd-af0c-39907febdc42 2012-09-28 08:07:38.560 UTC 6e3a57e1-23a5-4883-84d6-bf7a14a9b104 2012-06-21 14:06:12.125 UTC 04064c96-4b8d-4065-80e5-dfcabcb597de 2012-10-11 11:30:15.656 UTC f29cd8ec-6fe8-4962-9bbc-7c99ef58365d 2012-10-11 11:52:57.562 UTC f589ea15-b89d-415f-b16b-71d6b6abcc59 2012-09-28 08:41:41.388 UTC e38e9ac0-3bb8-427d-91c6-a40324275e42 2012-10-11 11:57:10.312 UTC 74dedf92-5055-45bd-995b-5b9df0a480a8 2012-10-05 11:40:27.651 UTC a67ab916-ab83-4d04-acc1-750adbebbea4 2012-10-11 12:03:39.671 UTC 18aac4ef-4208-458c-9dbb-9a2e5ee2f3f9 2012-06-21 14:24:41.784 UTC c6fd9af5-b7a7-460d-83c8-8122aa11253e 2012-09-28 08:43:02.498 UTC aabd83c8-3016-47ec-a1e2-e6d909289dfa 2012-06-22 08:38:17.934 UTC 6cefb97d-5207-40ec-847d-6b62cabd12cb 2012-06-15 06:56:38.363 UTC a6d860df-36bb-423a-a7e6-586818fccfc8 2012-09-28 08:06:19.732 UTC 19bbf243-d943-409c-9004-bb9df6c33349 2012-06-15 06:40:07.359 UTC e6545437-85f0-42a0-b54f-066b165d7ec6 2012-10-05 11:51:02.854 UTC c7b6d5dd-ddc3-4388-9f28-647b74d4f5f4 2012-06-15 06:54:00.692 UTC c4dc3143-373c-4d4e-adc3-8d8d84dd4a1e 2012-10-05 11:50:33.307 UTC e7b6d547-d809-4376-b8eb-f1208f5c07be 2012-10-11 12:06:45.921 UTC 138bd86c-fd0b-4db2-8092-1e9071940e2a 2012-09-28 08:45:04.951 UTC Maria Paula Balcázar-Vargas, Jonathan Giddy and Gerard Oostermeijer 2012-10-05 11:21:40.276 UTC 00e1ecf7-7f9c-4ee5-baea-9403570f30a4 2012-10-11 12:43:04.531 UTC d175cac8-5f8c-46ea-bb34-cd41954a2c5e 2012-10-11 11:59:17.375 UTC The time T required for the population to increase by a factor of Ro (net reproductive rate) e.g.: If Ro (net reproductive rate) = 5.56 and T (generation time) = 8.055 The average plant of the species Gentiana pneumonanthe in Terschelling in the year 1987 replaced itself with almost six new plants and took approximately 8.05 years to do so. 2012-10-11 12:06:44.656 UTC f92df37a-086b-4bbe-b7c2-42bc8660915e 2012-06-15 06:42:22.376 UTC f3629da2-038d-47c2-b494-15ce65b7df82 2012-10-11 12:02:50.281 UTC b5ec6e6b-d9bd-43b3-86d4-f33bd5640243 2012-10-05 11:37:41.839 UTC 6ed83074-6a5a-445c-bc2e-d0b4df878369 2012-10-11 11:35:56.890 UTC 724a9578-a6b6-44b9-9c02-fcf83e375a67 2012-09-28 08:40:05.123 UTC Generation time (T) 2012-10-05 11:22:20.526 UTC c4bce93c-e05f-433c-9f0f-36cb69f249e7 2012-10-05 11:49:57.167 UTC 91b0ec46-9891-4238-b9e2-57af321626a3 2012-10-30 10:31:22.988 UTC Bootstrap_of__observcensus_data11 plant year stage reprstr recruitment 2 1987 D 0 4 1987 J 0 5 1987 D 0 31 1987 V 0 36 1987 G 5 37 1987 J 0 41 1987 J 0 43 1987 S 0 46 1987 V 0 2 1988 V 0 4 1988 D 0 5 1988 V 0 9 1988 J 0 RJ 14 1988 J 0 RJ 2012-10-12 12:05:46.851 UTC Table containing demographic data of individuals in two year The input data (a .txt-file) has to have the format of a table containing the demographic data on a series of individuals in two years. Each individual has a table row for each year and is identified by a number (plant column in example). For the year specified in the column 'year', each individual has a certain life stage ('stage'). The stage codes can be chosen by the user, not longer than 5 characters. For all individuals, the number of offspring is specified in a chosen way (in the example, 'repstr' gives the number of flowers/fruits for each plant. In the example, it can be seen that only generative adults (stage=G) had flowers. Individuals without offspring have to be indicated by filling in '0' in this column, which cannot have empty cells. The column 'recruitment' specifies those individuals that are new to the population by means of a code. In the example, 'RS' is a new seedling, and 'RJ' a new juvenile plant. As can be seen in the example, no code is needed in this column for individuals that were already present. 2012-10-12 12:05:58.914 UTC stages11 S J V G D 2012-10-12 11:26:02.851 UTC Stage input port: 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 2012-10-12 11:25:20.445 UTC recruitedStages11startYear00plotTitle00bootstrapIterations00projection_matrix1 Projection matrix Output port: Creates a stage matrix from the input data. 2012-10-12 11:33:07.101 UTC 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 2012-10-12 11:32:28.195 UTC confidence_interval1 2.5% 97.5% 0.954955 1.468701 2012-10-05 14:22:04.620 UTC 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. 2012-10-12 12:24:22.164 UTC histogram0 Histogram plotting the frecuencies of the lambda values and the 95% confidence intervals resulting from the bootstrap analysis 2012-10-12 11:51:41.883 UTC trans10log0Preparecensus_data1recruited_stages1stages1start_year0trans111log00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity census_data 1 false stages 1 false recruited_stages 1 false start_year 0 false trans1 1 1 trans 1 1 log 0 0 false localhost 6311 false false census_data R_EXP stages STRING_LIST recruited_stages STRING_LIST start_year INTEGER trans1 R_EXP trans R_EXP log TEXT_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeAnalysisstages1trans11projection_matrix11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity trans1 1 false stages 1 false projection_matrix 1 1 false localhost 6311 false false trans1 R_EXP stages STRING_LIST projection_matrix R_EXP net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokePlotHistogramplot_title0a1ci1histogram00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity a 1 false ci 1 false plot_title 0 false ci 1 1 histogram 0 0 false localhost 6311 false false a R_EXP ci R_EXP plot_title STRING ci R_EXP histogram PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeConfidenceIntervaltrans11bootstrap_iterations0ci11a11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity trans1 1 false bootstrap_iterations 0 false a 1 1 ci 1 1 false localhost 6311 false false trans1 R_EXP bootstrap_iterations INTEGER a R_EXP ci R_EXP net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeDisplayTrans1input1output00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.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-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokePreparecensus_datacensus_dataPreparerecruited_stagesrecruitedStagesPreparestagesstagesPreparestart_yearstartYearAnalysisstagesstagesAnalysistrans1Preparetrans1PlotHistogramplot_titleplotTitlePlotHistogramaConfidenceIntervalaPlotHistogramciConfidenceIntervalciConfidenceIntervaltrans1Preparetrans1ConfidenceIntervalbootstrap_iterationsbootstrapIterationsDisplayTrans1inputPreparetrans1projection_matrixAnalysisprojection_matrixconfidence_intervalConfidenceIntervalcihistogramPlotHistogramhistogramtrans1DisplayTrans1outputlogPreparelog aa848d79-a9f7-497e-ba06-ee6feddc1da3 2012-06-27 06:00:45.433 UTC e98a3578-498e-4d02-9059-57c0cdf09e83 2012-06-27 05:56:22.181 UTC b8fe02f6-e147-4416-aeb4-6c4e54589512 2012-06-27 08:19:06.56 UTC fa80c538-bf1e-4c79-8c8a-b1a2b38e6056 2012-10-12 11:51:39.383 UTC 023bd080-a31d-46d1-a2b6-6f70249bf319 2012-10-18 15:02:35.315 UTC be9d3a0d-f115-4b04-9737-99f2a1dbed3b 2012-10-12 12:24:31.305 UTC 32904aa5-ff7a-4ad8-84a5-199a181f790f 2012-10-18 12:36:09.164 UTC b72b3f2b-ccd6-4520-9d21-c5fe7b69b11a 2012-06-27 07:51:21.217 UTC This Workflow was created by: Maria Paula Balcázar-Vargas, Jonathan Giddy and G. Oostermeijer 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 2012-10-12 11:04:44.914 UTC 1f58efab-3e46-4669-aadd-23ff3fdd62f4 2012-10-05 14:23:50.167 UTC 096ec245-ec16-40b5-bad6-7da6a1d58ce0 2012-10-19 03:55:10.475 UTC b60b6baa-0ced-481b-8ad5-2a9413a4fa2c 2012-10-12 12:10:41.867 UTC d646dd81-0fc6-46b9-891d-205f2e5a04a6 2012-10-12 11:51:58.883 UTC ea499285-044d-4fcf-b361-82e705bce535 2012-10-19 03:58:49.660 UTC ee769dbe-15e6-4f27-bf6f-feb4292a5a47 2012-10-19 04:38:45.215 UTC dc4a1937-f538-413c-ab37-4601c9f40c67 2012-10-18 14:55:56.19 UTC 3d8f7330-9060-42ea-94bc-f7c2878ce81a 2012-10-12 12:06:43.914 UTC 294ceddd-75cb-4181-b87c-3939b2ee08f4 2012-10-18 12:50:20.12 UTC b52aa862-b3ab-4602-a90f-14cf4a02b16f 2012-10-12 11:28:21.258 UTC 2ca6a24a-223b-435c-94df-72f7e34d19c0 2012-10-17 13:53:27.500 UTC 7f76e2ec-0550-44c2-ac29-00ab2441c50a 2012-06-27 05:33:43.841 UTC 105112aa-5a6e-4ace-9437-6421b756a88b 2012-10-19 04:44:24.480 UTC 75353ac2-b7b7-4842-bcd4-309605dac448 2012-10-12 12:01:54.789 UTC 0a45c68e-bb69-40fc-b695-3d6f8ebda19b 2012-10-18 08:38:21.536 UTC 8b334097-9faa-4020-b1b2-6e37585c8b30 2012-06-27 07:32:37.375 UTC 1da10601-a701-4542-ac84-b210462a5812 2012-06-27 06:02:05.237 UTC e0a9cd7d-9df2-484d-bfb5-293e3485e692 2012-10-18 05:47:47.58 UTC 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 intervals is one of the traditional tools to doing so (Caswell 2001). This workflow calculate bootstrap distributions of population growth rates (lambda), stage vectors, and projection matrix elements by randomly sampling with replacement from a stage-fate data frame of observed transitions. A detailed description of resampling methods to estimate confidence intervals for demographic estimates is described Caswell (2001, Chapter 12). 2012-10-12 12:10:40.351 UTC 4280c8bb-56c9-4795-b78a-106cdbf36e1a 2012-10-18 06:07:44.858 UTC fd4211f0-3d79-4602-8aaf-dc75784ede5a 2012-06-27 08:17:14.444 UTC 245ec9d7-9e92-478a-8214-d4f3f8a11e1e 2012-10-18 05:43:24.204 UTC 142a1764-0032-4f03-be33-a00089d40ab4 2012-06-27 05:53:46.91 UTC bd29b59d-e91a-48ae-8996-df31dd66ba7c 2012-06-27 07:39:46.142 UTC b6490574-28b7-488e-837e-31cb2c2fa5d9 2012-10-12 11:33:07.305 UTC a01daa2d-e2f8-4615-81cb-606b7c3243fb 2012-06-27 05:57:46.34 UTC Bootstrap of observed census transitions. 2012-10-05 14:10:39.479 UTC 208fcd5f-7e4f-4c74-8014-864e118dac6d 2012-10-12 11:26:23.664 UTC aa6cba9d-a378-40a3-aeb7-c42870cdb776 2012-10-19 04:02:43.872 UTC c444c1e9-3edc-4516-bae8-7863671fae6b 2012-10-05 14:07:31.729 UTC 96776de8-09fd-4460-8184-862440d9756e 2012-10-05 14:13:16.854 UTC Workflow4census_data11stageColumnHeader00allStages1ParseStagescensus_data1stage_column_header0all_stages11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity census_data 1 false stage_column_header 0 false all_stages 1 1 false localhost 6311 false false census_data R_EXP stage_column_header STRING all_stages STRING_LIST net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeParseStagescensus_datacensus_dataParseStagesstage_column_headerstageColumnHeaderallStagesParseStagesall_stages e5f31b22-95ac-436a-a9c5-1ae57fe52f5e 2012-07-13 06:18:59.775 UTC 8af0f726-006f-4a83-9eec-9cdc97dcb9d0 2012-07-06 06:48:37.151 UTC 90ada9ed-5f4a-4e0a-89a3-1d037e18ba01 2012-10-17 11:15:32.500 UTC fea78a2d-fde4-46ab-9a54-741b27dc5a90 2012-07-11 12:32:16.405 UTC 0e09a93a-32f2-49b8-8a35-7499aa48b3a2 2012-07-11 12:30:56.575 UTC 7273a214-8b3e-45b9-9e68-d785a84e8f67 2012-07-11 14:11:47.824 UTC 8a5082d0-529b-4205-b3e2-8e0cd8812239 2012-07-06 06:56:06.356 UTC Sensitivity_and_Elasidentifier00stage_matrix11sensitivity_matrix1sensitivityPlot0elasticity_matrix1elasticityPlot0SensitivityPlotidentifier0stage_matrix1sensitivity_plot00sensitivity_matrix11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false identifier 0 false sensitivity_matrix 1 1 sensitivity_plot 0 0 false localhost 6311 false false stage_matrix R_EXP identifier STRING sensitivity_matrix R_EXP sensitivity_plot PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeElasticityPlotidentifier0stage_matrix1elasticity_plot00elasticity_matrix11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false identifier 0 false elasticity_matrix 1 1 elasticity_plot 0 0 false localhost 6311 false false stage_matrix R_EXP identifier STRING elasticity_matrix R_EXP elasticity_plot PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSensitivityPlotidentifieridentifierSensitivityPlotstage_matrixstage_matrixElasticityPlotidentifieridentifierElasticityPlotstage_matrixstage_matrixsensitivity_matrixSensitivityPlotsensitivity_matrixsensitivityPlotSensitivityPlotsensitivity_plotelasticity_matrixElasticityPlotelasticity_matrixelasticityPlotElasticityPlotelasticity_plot 663c6c33-b50d-4927-ab0e-1ef45940f224 2013-01-28 21:51:13.360 UTC b78e8e6c-0769-40b3-b651-d08340980d14 2013-01-15 21:09:58.530 UTC ba2da66c-56ad-4e93-b3e3-5acc4dbc3ec0 2013-01-15 21:30:56.337 UTC Sensitivity and Elasticity 2013-01-15 21:10:36.4 UTC a97e4075-2be5-4271-8367-aa9258eda699 2013-01-15 21:10:40.245 UTC StageMatrix_FromCensinitialYear00 2001 2012-02-29 15:13:45.353 UTC stages11recruitedStages11census_data11stage_matrix1report0StageMatrix_FromCensusDatastages1census_data1recruited_stages1start_year0stage_matrix11report00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity census_data 1 false start_year 0 false stages 1 false recruited_stages 1 false stage_matrix 1 1 report 0 0 false localhost 6311 false false census_data R_EXP start_year INTEGER stages STRING_LIST recruited_stages STRING_LIST stage_matrix R_EXP report TEXT_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeStageMatrix_FromCensusDatastagesstagesStageMatrix_FromCensusDatacensus_datacensus_dataStageMatrix_FromCensusDatarecruited_stagesrecruitedStagesStageMatrix_FromCensusDatastart_yearinitialYearstage_matrixStageMatrix_FromCensusDatastage_matrixreportStageMatrix_FromCensusDatareport 997307f3-021e-407f-a896-f185175ac167 2012-02-29 14:36:46.988 UTC 67897c27-cf75-45e5-a87a-febdcd4d1595 2012-07-11 12:40:29.691 UTC 6b497e85-e36a-4242-a276-4e32df5e0c8c 2012-02-24 10:05:15.21 UTC da047630-e3d2-4abe-8ccf-e0dca0267b9f 2012-03-22 16:12:53.143 UTC 8ef046ff-93f4-451b-8ef5-c1272aad4ffb 2012-03-22 23:15:53.859 UTC 41251728-c268-4ad9-a368-2396b9ca8e14 2012-02-27 16:57:33.980 UTC 6d7d98ff-ed71-4281-9e4d-423f4c8859d1 2012-03-30 12:27:09.543 UTC 1666ccee-7aba-415b-a788-a108241e28d7 2012-03-30 12:07:00.309 UTC b916b8e3-29e0-4af1-ad36-10ee41ac904d 2012-06-27 07:56:30.606 UTC 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UTC 96a32925-ddb2-4cb9-bad3-c2063d7b8028 2012-07-13 08:43:42.660 UTC 31971e7a-db26-47a1-8a5e-78479a3a04ba 2012-03-30 12:01:54.306 UTC f91bafb2-5383-472f-b74c-012bbb2ebb74 2012-07-13 07:44:46.374 UTC 4853919f-8dd9-480a-ab79-132c36c72b93 2012-02-27 16:52:39.396 UTC b57e9fd4-f7b4-4189-abfa-febbd95f33ea 2012-03-30 10:44:17.387 UTC 9dc1f134-a2b3-4c4e-ae7a-15f45a8d89ec 2012-10-10 15:14:44.283 UTC 6f264a9e-96d9-4686-a590-4c9113764624 2012-10-20 06:47:21.780 UTC e9f677a8-f57f-4531-ad17-8bcdfae6ef07 2012-03-30 10:53:03.557 UTC 03a50147-2b3b-473f-addd-750829af4102 2012-02-24 09:54:31.471 UTC d86f9e3f-a0f5-49cf-88f4-bbef7790af5b 2012-02-29 14:53:19.163 UTC 500a7392-736d-4ed5-a915-3fe4b330f0f6 2012-02-24 09:57:43.590 UTC e270fb88-b9ee-4d7f-ab6c-3f7b48567896 2012-03-30 10:07:22.211 UTC ffea6962-1efa-4873-a520-ebc2080e65f6 2012-03-30 12:57:41.3 UTC 30b2faf0-b5fd-4ba7-a30d-0ea8a042ebb3 2012-03-30 13:43:36.345 UTC 6d8478f3-f2cd-4124-a549-d2b7f6d2cfba 2012-07-13 09:12:53.413 UTC 6f1812f3-f496-4865-9cb8-b1c2a0beec12 2012-02-29 14:29:48.341 UTC 0bd11ab8-5887-4928-be0f-5722465d7557 2012-10-18 11:17:02.830 UTC 2111849d-7efc-4639-b864-46583baf56e4 2012-10-18 11:26:40.703 UTC ac149e0f-1fee-442e-8a75-3efc391f71a5 2012-03-30 13:22:39.393 UTC 47184b40-b5bf-4756-a748-48a85656755a 2012-07-11 12:58:17.333 UTC Eigen_analysisstage_matrix11 The stage matrix file input port: 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. 2012-10-10 09:01:24.171 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 2012-10-03 13:50:09.593 UTC speciesName00 Gentiana pneumonanthe 2012-10-10 08:45:54.921 UTC Species name input port: In this input port comes the title of the bar plot that will be generated with the analysis. As an example, it can be the name of the species or the name of the place where the research has been conducted, between others. 2012-10-10 08:46:17.484 UTC barPlot0 2012-10-10 11:25:12.531 UTC A bar plot which shows the stable stage distribution (w) of the analyzed matrix. It plots the proportion of individuals per stage. 2012-10-10 11:25:10.921 UTC projectionMatrix0 Projection matrix Output port: Creates a grid of colored rectangles to display the stage matrix input. 2012-10-10 11:16:38.125 UTC Projection matrix 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 2012-10-12 11:31:11.508 UTC elasticityMatrix0 Elasticity matrix S J V G D S 0.0000 0.0000 0.0000 0.0368 0.0000 J 0.0066 0.0013 0.0000 0.1571 0.0000 V 0.0302 0.0633 0.2732 0.0030 0.0054 G 0.0000 0.0922 0.0824 0.1971 0.0223 D 0.0000 0.0082 0.0196 0.0000 0.0005 2012-10-10 11:31:52.234 UTC The output port: Elasticity matrix Creates a grid of colored rectangles to display the elasticities. The elasticities of λ with respect to the stage are often interpreted as the “contributions” of each of the stages to λ. This interpretation relies on the demonstration, by de Kroon et al (1986), that the elasticites of the λ with respect to the stage, always sum to 1. For further information see: Literature: de Kroon, Hans, Anton Plaisier, Jan van Groenendael, and Hal Caswell. 1986. Elasticity: The Relative Contribution of Demographic Parameters to Population Growth Rate. Ecology 67:1427–1431 Caswell, H. 2001. Matrix population models, construction, analysis and interpretation. Second edition. Sinauer Associates, Inc Publishers. 2012-10-10 11:26:17.171 UTC sensitivityMatrix10 The sensitivity matrix output: Creates a grid of colored rectangles to display the sensitivities. In this graph are only shown the sensitivities of the actual transitions. 2012-10-10 11:44:24.250 UTC Sensitivity matrix S J V G D S 0.0000 0.0000 0.0000 0.0059 0.0000 J 0.1413 0.1650 0.0000 0.0228 0.0000 V 0.0808 0.0944 0.3753 0.0130 0.0078 G 0.0000 2.8526 11.339 0.3942 0.2385 D 0.0000 0.3398 1.3509 0.0000 0.0284 2012-10-10 11:50:32.796 UTC sensitivityMatrix20 The sensitivity matrix output 2 Creates a grid of colored rectangles to display the sensitivities. In this graph are shown the sensitivities of the all posible transitions. 2012-10-10 11:47:16.562 UTC Sensitivity matrix S J V G D S 0.037 0.043 0.171 0.006 0.004 J 0.141 0.165 0.656 0.023 0.014 V 0.080 0.094 0.375 0.013 0.008 G 2.442 2.853 11.34 0.394 0.239 D 0.291 0.340 1.351 0.047 0.028 2012-10-10 11:55:22.796 UTC eigenanalysis1 $lambda1 [1] 1.237596 $stable.stage S J V G D 0.14143023 0.16520742 0.65671474 0.02283244 0.01381517 $sensitivities S J V G D S 0.00000000 0.00000000 0.0000000 0.005956842 0.00000000 J 0.14133539 0.16509663 0.0000000 0.022817127 0.00000000 V 0.08083208 0.09442153 0.3753343 0.013049498 0.00789583 G 0.00000000 2.85265996 11.3395869 0.394250955 0.23854854 D 0.00000000 0.33985571 1.3509578 0.000000000 0.02841982 $elasticities S J V G D S 0.000000000 0.000000000 0.00000000 0.036898276 0.0000000000 J 0.006612271 0.001334011 0.00000000 0.157150348 0.0000000000 V 0.030286005 0.063324284 0.27322221 0.003012487 0.0054893301 G 0.000000000 0.092200046 0.08246333 0.197189845 0.0223977311 D 0.000000000 0.008238288 0.01964877 0.000000000 0.0005327586 $repro.value S J V G D 1.000000 3.830406 2.190674 66.184553 7.884991 $damping.ratio [1] 2.092025 2012-10-10 11:37:42.546 UTC Eigen analysis output The Eigen analysis results are a set of demographic statistics: 1) Lambda or dominant eigenvalue: The population will be stable, grow or decrease at a rate given by lambda: eg: λ = 1 (population is stable), λ > 1 (population is growing) and finally λ < 1 (populatiopn is decreasing) . E.g. The projected population growth rate (λ) is 1.237, meaning that the population is projected to increase with 23% per year if these model parameters remain unchanged. 2) The stable stage distribution: It is the proportion of the number of individuals per stage and it is given by (w). Elasticity and Sensitivity: Sensitivity and elasticity analyses are prospective analyses. 3) The sensitivity matrix: The sensitivity gives the effect on λ of changes in any entry of the matrix, including those that may, an a given context, be regarded as fixed at zero or some other value. The derivative tells what would happened to λ if aij was to change, not whether, or in what direction, or how much, aij actually change. The hypothetical results of such impossible perturbations may or may not be of interest, but they are not zero. It is up to you to decide whether they are useful (Caswell 2001). When comparing the λ-sensitivity values for all matrix elements one can find out in what element a certain increase has the biggest impact on λ. However, a 0.01 increase in a survival matrix element is hard to compare to a 0.01 increase in a reproduction matrix element, because the latter is not bound between 0 and 1 and can sometimes take high values. Increasing matrix element a14 (number of S (seedlings) the next year produced by an G (Reproductive individuals)) with 0.01 from 7.666 to 7.676 does not have a noticeable effect on λ. For comparison between matrix elements it can therefore be more insightful to look at the impact of proportional changes in elements: by what percentage does λ change if a matrix element is changed by a certain percentage? This proportional sensitivity is termed elasticity (Description based on Oostermeijer data, based on Jongejans & de Kroon 2012). 4) The Elasticity matrix: The elasticities sum to 1 across the whole matrix (Caswell 1986; de Kroon et al. 1986; Mesterton-Gibbons 1993) and can be interpreted as proportional contributions of the corresponding vital rates to the matrix (see van Groenendael et al. 1994). 5) Reproductive value (v): scaled so v[1]=1. To what extent will a plant or animal of a determinate category or stage , contribute to the ancestry of future generation. 6) The damping ratio: it can be considered as a measure of the intrinsic resilience of the population, describing how quickly transient dynamics decay following disturbance or perturbation regardless of population structure, the larger the p, the quicker the population converges. Those statistics are function of the vital rates, and througt them of biological and environmental variables. For further details see: Caswell, H. 1986. Life cycle models for plants. Lectures on Mathematics in the Life Sciences 18: 171-233. Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts. Horvitz, C., D.W. Schemske, and Hal Caswell. 1997. The relative "importance" of life-history stages to population growth: Prospective and retrospective analyses. In S. Tuljapurkar and H. Caswell. Structured population models in terrestrial and freshwater systems. Chapman and Hall, New York. Jongejans E. & H. de Kroon. 2012. Matrix models. Chapter in Encyclopedia of Theoretical Ecology (eds. Hastings A & Gross L) University of California, p415-423 de Kroon, H. J., A. Plaiser, J. van Groenendael, and H. Caswell. 1986. Elasticity: The relative contribution of demographic parameters to population growth rate. Ecology 67: 1427-1431. Mesterton-Gibbons, M. 1993. Why demographic elasticities sum to one: A postscript to de Kroon et al. Ecology 74: 2467-2468. van Groenendael, J., H. de Kroon, S. Kalisz, and S. Tuljapurkar. 1994. Loop analysis: Evaluating life history pathways in population projection matrices. Ecology 75: 2410-2415. 2012-10-11 09:08:40.125 UTC ProjectionMatrixstage_matrix1plot_title0plot_size0plot_image00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity plot_title 0 false stage_matrix 1 false plot_size 0 false plot_image 0 0 false localhost 6311 false false plot_title STRING stage_matrix R_EXP plot_size INTEGER plot_image PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeProjection_matrixvalue00net.sf.taverna.t2.activitiesstringconstant-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.stringconstant.StringConstantActivity Projection matrix net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeEigenanalysisNonZeroElementsstage_matrix1eigenanalysis11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false eigenanalysis 1 1 false localhost 6311 false false stage_matrix R_EXP eigenanalysis R_EXP net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeEigenanalysisAllElementsstage_matrix1eigenanalysis11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false eigenanalysis 1 1 false localhost 6311 false false stage_matrix R_EXP eigenanalysis R_EXP net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeBarPloteigenanalysis1bar_plot_title0bar_plot_image00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity bar_plot_title 0 false eigenanalysis 1 false bar_plot_image 0 0 false localhost 6311 false false bar_plot_title STRING eigenanalysis R_EXP bar_plot_image PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeElasticity_matrixvalue00net.sf.taverna.t2.activitiesstringconstant-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.stringconstant.StringConstantActivity Elasticity matrix net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeElasticityMatrixeigenanalysis1plot_title0plot_size0plot_image00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity plot_title 0 false eigenanalysis 1 false plot_size 0 false plot_image 0 0 false localhost 6311 false false plot_title STRING eigenanalysis R_EXP plot_size INTEGER plot_image PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSensitivityMatrixeigenanalysis1plot_title0plot_size0plot_image00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity plot_title 0 false eigenanalysis 1 false plot_size 0 false plot_image 0 0 false localhost 6311 false false plot_title STRING eigenanalysis R_EXP plot_size INTEGER plot_image PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSensitivity_matrix_1value00net.sf.taverna.t2.activitiesstringconstant-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.stringconstant.StringConstantActivity Sensitivity matrix 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSensitivity_matrix_2value00net.sf.taverna.t2.activitiesstringconstant-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.stringconstant.StringConstantActivity Sensitivity matrix 2 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSensitivityMatrix_2eigenanalysis1plot_title0plot_size0plot_image00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity plot_title 0 false eigenanalysis 1 false plot_size 0 false plot_image 0 0 false localhost 6311 false false plot_title STRING eigenanalysis R_EXP plot_size INTEGER plot_image PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeCalculatePlotSizestage_matrix1plot_size00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false plot_size 0 0 false localhost 6311 false false stage_matrix R_EXP plot_size INTEGER net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeProjectionMatrixstage_matrixstage_matrixProjectionMatrixplot_titleProjection_matrixvalueProjectionMatrixplot_sizeCalculatePlotSizeplot_sizeEigenanalysisNonZeroElementsstage_matrixstage_matrixEigenanalysisAllElementsstage_matrixstage_matrixBarPloteigenanalysisEigenanalysisNonZeroElementseigenanalysisBarPlotbar_plot_titlespeciesNameElasticityMatrixeigenanalysisEigenanalysisNonZeroElementseigenanalysisElasticityMatrixplot_titleElasticity_matrixvalueElasticityMatrixplot_sizeCalculatePlotSizeplot_sizeSensitivityMatrixeigenanalysisEigenanalysisNonZeroElementseigenanalysisSensitivityMatrixplot_titleSensitivity_matrix_1valueSensitivityMatrixplot_sizeCalculatePlotSizeplot_sizeSensitivityMatrix_2eigenanalysisEigenanalysisAllElementseigenanalysisSensitivityMatrix_2plot_titleSensitivity_matrix_2valueSensitivityMatrix_2plot_sizeCalculatePlotSizeplot_sizeCalculatePlotSizestage_matrixstage_matrixbarPlotBarPlotbar_plot_imageprojectionMatrixProjectionMatrixplot_imageelasticityMatrixElasticityMatrixplot_imagesensitivityMatrix1SensitivityMatrixplot_imagesensitivityMatrix2SensitivityMatrix_2plot_imageeigenanalysisEigenanalysisNonZeroElementseigenanalysis This Workflow was created by: Maria Paula Balcázar-Vargas, Jonathan Giddy and G. Oostermeijer 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 using and 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. 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6315c9ba-381a-44ce-82cd-ea6f5aae35d5 2012-07-13 09:27:13.462 UTC 3a09aae3-3fa4-44e7-a064-e02418cffa92 2012-10-03 13:54:56.46 UTC 31de0f25-8b52-4b7c-a868-4eb26381b768 2012-07-11 16:08:08.989 UTC 8eb6139c-8930-43f8-bd9a-5603f973b51b 2012-09-26 14:28:53.828 UTC e0424e01-ed30-4c94-95b8-143751fca7a2 2012-07-11 16:07:29.317 UTC 48960c0c-1f5d-4e1c-a5d0-270927af1440 2012-10-10 11:59:42.921 UTC a70a1e5c-8940-4703-bffa-a89da5537fdb 2012-09-21 05:10:47.15 UTC 40f5c83b-f16c-4564-a6db-81a91ff724c0 2012-10-10 12:02:09.375 UTC 7f69fad0-a2a7-42ad-aebe-8b0aa109d549 2012-10-10 10:51:54.312 UTC 687a5ff0-61b3-4784-a263-6e7974626f6d 2012-11-26 16:03:00.764 UTC 98e93c16-3991-466d-9ade-6f082f2a3a1c 2012-10-10 11:25:12.781 UTC d200135a-b430-4ff3-aec3-cb4139f821ca 2012-10-10 11:55:23.78 UTC 4988e019-bf93-40bd-b095-6a33aec7e968 2012-07-13 08:39:49.679 UTC Eigen analysis 2012-09-26 12:46:27.453 UTC 27fa10ea-ca41-4311-b50c-eb7e2fe66489 2012-07-11 15:49:21.753 UTC The Eigen analysis results are a set of demographic statistics: 1) Lambda or dominant eigenvalue: The population will be stable, grow or decrease at a rate given by lambda: eg: λ = 1 (population is stable), λ > 1 (population is growing) and finally λ < 1 (populatiopn is decreasing) . 2) The stable stage distribution: It is the proportion of the number of individuals per stage and it is given by (w). Elasticity and Sensitivity: Sensitivity and elasticity analyses are prospective analyses. 3) The sensitivity matrix: The sensitivity gives the effect on λ of changes in any entry of the matrix, including those that may, an a given context, be regarded as fixed at zero or some other value. The derivative tells what would happened to λ if aij was to change, not whether, or in what direction, or how much, aij actually change. The hypothetical results of such impossible perturbations may or may not be of interest, but they are not zero. It is up to you to decide whether they are useful (Caswell 2001). When comparing the λ-sensitivity values for all matrix elements one can find out in what element a certain increase has the biggest impact on λ. However, a 0.01 increase in a survival matrix element is hard to compare to a 0.01 increase in a reproduction matrix element, because the latter is not bound between 0 and 1 and can sometimes take high values. Increasing matrix element a14 (number of S (seedlings) the next year produced by an G (Reproductive individuals)) with 0.01 from 7.666 to 7.676 does not have a noticeable effect on λ. For comparison between matrix elements it can therefore be more insightful to look at the impact of proportional changes in elements: by what percentage does λ change if a matrix element is changed by a certain percentage? This proportional sensitivity is termed elasticity (Description based on Oostermeijer data, based on Jongejans & de Kroon 2012). 4) The Elasticity matrix: The elasticities sum to 1 across the whole matrix (Caswell 1986; de Kroon et al. 1986; Mesterton-Gibbons 1993) and can be interpreted as proportional contributions of the corresponding vital rates to the matrix (see van Groenendael et al. 1994). 5) Reproductive value (v): scaled so v[1]=1. To what extent will a plant or animal of a determinate category or stage , contribute to the ancestry of future generation. 6) The damping ratio: it can be considered as a measure of the intrinsic resilience of the population, describing how quickly transient dynamics decay following disturbance or perturbation regardless of population structure, the larger the p, the quicker the population converges. Those statistics are function of the vital rates, and througt them of biological and environmental variables. For further details see: Caswell, H. 1986. Life cycle models for plants. Lectures on Mathematics in the Life Sciences 18: 171-233. Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts. Horvitz, C., D.W. Schemske, and Hal Caswell. 1997. The relative "importance" of life-history stages to population growth: Prospective and retrospective analyses. In S. Tuljapurkar and H. Caswell. Structured population models in terrestrial and freshwater systems. Chapman and Hall, New York. Jongejans E. & H. de Kroon. 2012. Matrix models. Chapter in Encyclopedia of Theoretical Ecology (eds. Hastings A & Gross L) University of California, p415-423 de Kroon, H. J., A. Plaiser, J. van Groenendael, and H. Caswell. 1986. Elasticity: The relative contribution of demographic parameters to population growth rate. Ecology 67: 1427-1431. Mesterton-Gibbons, M. 1993. Why demographic elasticities sum to one: A postscript to de Kroon et al. Ecology 74: 2467-2468. van Groenendael, J., H. de Kroon, S. Kalisz, and S. Tuljapurkar. 1994. Loop analysis: Evaluating life history pathways in population projection matrices. Ecology 75: 2410-2415. 2012-10-10 11:22:42.62 UTC 9159d5c8-8084-403e-9941-37a0babea12d 2012-07-13 08:10:15.746 UTC 6ad79f50-2ace-4bb4-b896-fc34810868c6 2012-09-26 13:51:56.437 UTC Fundamental_matrixstage_matrix11 The stage matrix file input port: 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. 2012-10-10 12:44:04.359 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 2012-10-03 14:34:31.812 UTC rows_fecundity11 1 2 2012-10-10 12:36:41.765 UTC To perform the fundamental matrix analysis N, the row(s) in which the recruitment values are found, should be selected. In the example of the Gentiana species (Oostermeijer et al. 1996. The Journal of Ecology): Selected lines are S and J (seedlings and juveniles, these stages receive recruits each year from the stage G): therefore, the numbers 1 and 2 are used to identify these rows. 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 2012-10-11 12:34:12.781 UTC columns_fecundity11 4 2012-10-10 12:36:25.328 UTC To perform the fundamental matrix analysis N, the column(s) in which the fecundity values are found, should be selected. In the example of the Gentiana species (Oostermeijer et al. 1996. The Journal of Ecology): The selected column is G (reproductive individuals): therefore number 4 will be used to identify the fecundity column (G). 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 2012-10-10 12:38:41.968 UTC fundamental_matrix1 The fundamental matrix workflow gives the basic information on age-specific survival, this includes the mean, variance and coefficient of variation (cv) of the time spent in each stage class and the mean and variance of the time to death. Fundamental matrix (N): is the mean of the time spent in each stage class E.g.:For our Gentiana example means a J plants will spends, on average, about 1 year as a Juvenile, 11 as vegetative plant, less than a year a reproductive and dormant plant. Variance (var): is the variance in the amount of time spent in each stage class. Coefficient of variation (cv): is the coefficient of variation of the time spent in each class (sd/mean- the ratio of the standart deviation to the mean). Meaneta: is the mean of time to death, of life expectancy of each stage. E.g.:The mean age at death is the life expectancy; the life expectancty of a new individual seedling is 8 years. Vareta: is the variance of time to death For further information please see Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts. 2012-10-10 12:59:24.859 UTC $N S J V G D S 1.00000000 0.0000000 0.0000000 0.0000000 0.0000000 J 0.05848485 1.0101010 0.0000000 0.0000000 0.0000000 V 6.88603853 11.9918695 13.3528343 10.0128734 12.9527790 G 0.20805087 0.4661775 0.3904662 2.9174703 0.6909984 D 0.12868882 0.2520032 0.2460596 0.1845124 1.2624386 $var S J V G D S 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 J 0.05624588 0.01020304 0.0000000 0.0000000 0.0000000 V 129.59269836 164.45409019 164.9453506 157.1299726 165.1853617 G 0.96262845 2.03661916 1.7354172 5.5941629 2.8634574 D 0.17967383 0.32076827 0.3146653 0.2473139 0.3313126 $cv S J V G D S 0.000000 NaN NaN NaN NaN J 4.055104 0.100000 NaN NaN NaN V 1.653183 1.069388 0.9618261 1.2519033 0.9922539 G 4.715848 3.061284 3.3737933 0.8107017 2.4488847 D 3.293833 2.247448 2.2797338 2.6952479 0.4559411 $meaneta S J V G D 8.281263 13.720151 13.989360 13.114856 14.906216 $vareta S J V G D 143.2630 181.0125 181.4811 177.0582 181.0617 2012-10-10 12:56:28.203 UTC fundamental_matrixcolumns1rows1stage_matrix1a11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false rows 1 false columns 1 false a 1 1 false localhost 6311 false false stage_matrix R_EXP rows DOUBLE_LIST columns DOUBLE_LIST a R_EXP net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokefundamental_matrixcolumnscolumns_fecundityfundamental_matrixrowsrows_fecundityfundamental_matrixstage_matrixstage_matrixfundamental_matrixfundamental_matrixa Maria Paula Balcázar-Vargas, Jonathan Giddy and Gerard Oostermeijer 2012-10-10 12:20:09.765 UTC 808d955e-b242-463f-9ab0-3e5526d24391 2012-10-30 07:03:42.367 UTC fae44800-e1c6-41e0-9784-aa2a44d75751 2012-09-28 12:28:31.62 UTC 88ba65a7-98b4-4f47-89d6-836e7c1fa9c1 2012-09-26 14:26:56.765 UTC 02ba332a-4054-4142-88ef-9d17ab76aa38 2012-10-10 12:43:36.218 UTC Fundamental matrix 2012-09-26 14:05:47.140 UTC 7bb65b13-5427-4b64-bf88-b97ba2bd87c8 2012-10-11 11:57:47.796 UTC 29186c39-3022-4d52-90d0-8f7fd7bcef27 2012-03-30 15:47:32.455 UTC 348192ff-7a44-4a75-a3b4-af3adf25ea2b 2012-04-27 06:08:00.914 UTC db524ca2-dc25-4036-b866-fb6f2449f18b 2012-09-28 15:07:54.812 UTC 482df01a-aece-421a-87e6-87c6fed3098c 2012-04-27 06:16:05.128 UTC 5ad8da23-95d6-4f62-b1fc-82689c17ffd7 2012-03-30 15:52:40.327 UTC 068f4c47-ac93-41d1-928b-e5313ddf7dc9 2012-03-30 15:27:01.885 UTC 73569abf-949f-48f6-92cf-9d91b9b9fc5b 2012-10-10 12:59:04.609 UTC e4172fb8-fbf4-4a13-aad8-b4ea9a55fcaa 2012-09-28 15:08:43.718 UTC 26a58a12-7058-4ecc-8626-8f4208df050d 2012-04-27 06:37:14.879 UTC 85e5205a-e20f-42a5-85b6-857a1bcf6751 2012-04-27 10:04:31.201 UTC 4953ee13-f5cd-4472-9bc9-19401eef9af4 2012-10-11 12:39:20.734 UTC ae90fa83-15d4-42a1-b5fc-75f5b0540555 2012-09-26 14:28:58.906 UTC 8237763e-e3d3-4a89-8154-c0e7ebe6ac99 2012-03-30 15:58:10.751 UTC 3da39227-d90d-44b6-93e6-a5c6c6635c28 2012-03-30 15:42:09.674 UTC 6a5c611c-cfcf-4964-9c89-f254b5521e15 2012-04-26 16:16:27.814 UTC 5af80ab8-1db1-4552-a538-92256862e14e 2012-09-26 14:20:30.218 UTC 64fd75c1-1560-485f-9e95-f556f284ea0a 2012-10-03 14:35:02.156 UTC 5d52fc73-4937-4ab4-a7fb-ff38d0384a11 2012-10-30 07:01:53.978 UTC 553eb85d-393a-488a-84ae-6190b23e80cc 2012-03-30 15:56:49.708 UTC 09f91d33-9f50-4983-96c9-1335ed1f92b4 2012-04-27 06:19:38.161 UTC The fundamental matrix workflow gives the basic information on age-specific survival, this includes the mean, variance and coefficient of variation (cv) of the time spent in each stage class and the mean and variance of the time to death. Fundamental matrix (N): is the mean of the time spent in each stage class Variance (var): is the variance in the amount of time spent in each stage class. Coefficient of variation (cv): is the coefficient of variation of the time spent in each class (sd/mean- the ratio of the standart deviation to the mean) Meaneta: is the mean of time to death, of life expectancy of each stage. Vareta: is the variance of time to death For more information see: Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts (pag 118-120) 2012-10-10 12:35:37.812 UTC 573c3e8c-5192-4dcd-a1b1-4c69d93185d8 2012-10-03 13:17:26.78 UTC 514adf8d-61fa-467f-9395-f5979832478d 2012-10-10 12:42:45.593 UTC 1e66a160-d9a3-485d-b31e-cac2c674a296 2012-03-30 15:59:19.350 UTC 03f85255-cd88-4d89-b6b6-40c5c6eae7f5 2012-10-10 12:59:26.406 UTC 252c40b1-ea29-47e7-bf0f-3af7aab03d0c 2012-10-10 12:47:26.968 UTC f4dfa9b1-5f6a-46a6-a7ab-5a660a1735eb 2012-04-27 06:33:44.26 UTC e1d1c2a5-29c1-4939-b4b2-0980a2dceaea 2012-10-10 12:30:26.453 UTC 554ae0b3-6ab6-4242-9607-94afd162770d 2012-10-03 13:28:40.515 UTC 67f6d5fc-c41e-4a15-aa70-06a91d869ca3 2012-09-26 14:09:13.187 UTC 044bc39f-c00c-4683-aec3-9b467473c894 2012-10-10 12:44:06.562 UTC 1d18264c-9c31-446e-b5fe-faf419d8697b 2012-10-03 14:35:35.765 UTC 028669ad-d798-4123-bf4a-b99951b64e18 2012-10-11 11:46:36.203 UTC 5ddec6d6-2ef8-4a1b-8905-4ebc313e8a5f 2012-03-30 15:53:45.576 UTC e4416b66-0517-4982-9ffc-07d9225b4cb4 2012-04-27 10:20:27.369 UTC Survival_CurvestageMatrix11plotTitle00fecundityRows11fecundityCols11survivalCurvePlot0SurvivalCurvePlotsurvival_curve1plot_title0survival_curve_plot00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity survival_curve 1 false plot_title 0 false survival_curve_plot 0 0 false localhost 6311 false false survival_curve R_EXP plot_title STRING survival_curve_plot PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSurvivalCurveAnalysisstage_matrix1fecundity_rows1fecundity_cols1survival_curve11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false fecundity_rows 1 false fecundity_cols 1 false survival_curve 1 1 false localhost 6311 false false stage_matrix R_EXP fecundity_rows INTEGER_LIST fecundity_cols INTEGER_LIST survival_curve R_EXP net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSurvivalCurvePlotsurvival_curveSurvivalCurveAnalysissurvival_curveSurvivalCurvePlotplot_titleplotTitleSurvivalCurveAnalysisstage_matrixstageMatrixSurvivalCurveAnalysisfecundity_rowsfecundityRowsSurvivalCurveAnalysisfecundity_colsfecundityColssurvivalCurvePlotSurvivalCurvePlotsurvival_curve_plot 6acb547e-2799-455a-9b3d-6226cdc1e412 2013-01-11 16:59:47.395 UTC bca1bd86-921e-4a5e-8781-c512073d78f3 2013-01-11 16:24:51.231 UTC d889120e-eaaa-42aa-be59-547f441203cf 2013-01-11 16:47:59.509 UTC ee31a943-928e-40bb-92ec-c7800db0159a 2013-01-11 16:52:44.937 UTC e42c0737-b2cc-4e9a-a418-e7e50dc76d48 2013-01-10 15:39:38.567 UTC 5238b5e0-4a59-4eef-9bda-ed668dbacd0e 2013-01-11 16:55:31.203 UTC 177eccf2-2fce-4c09-94a0-c2c5fa77ecee 2013-01-10 15:41:43.396 UTC e4074c6e-68d0-4109-aa2f-fb9e2fc9fc16 2013-01-11 17:23:58.83 UTC 9bb967c3-0f4d-41ec-8df1-4e41927366e8 2013-01-10 15:40:37.18 UTC f0a5f728-ae5b-4bc7-9c1a-c3ae7aaea52e 2013-01-11 16:26:13.108 UTC 7a05fca2-4829-4703-821a-15c82eeb29b8 2013-01-11 16:15:08.904 UTC e8a0eb32-29ea-4d29-b1c0-109609bbf238 2013-01-11 16:20:59.585 UTC 4d6db02f-1eb1-424b-843e-f9fd3d691ba5 2013-01-10 16:40:49.38 UTC c9aa8a4f-e132-4e41-a439-970908abd5b7 2013-01-11 16:50:21.663 UTC a906ae6a-90f1-419a-ba1d-ab69c2e93bc9 2013-01-10 16:10:54.72 UTC c3ee08d1-b7e2-4c53-8a53-3c5cdf393783 2013-01-09 17:16:37.963 UTC e406cf24-e16f-412b-8582-3d93c7f480f8 2013-01-11 16:57:10.541 UTC a7f508b3-cf63-4714-a790-29aa80fea05e 2013-01-11 16:11:35.296 UTC Survival Curve 2013-01-10 15:39:58.997 UTC 79a3f183-ee97-44d4-a995-2d3ee3ea86e5 2013-01-11 16:27:47.11 UTC 7ce04ea6-9907-4d42-b256-746a89ed4a0e 2013-01-11 17:25:34.447 UTC cb903810-0b79-43bb-add5-05a172675650 2013-01-11 16:10:12.451 UTC Maria Paula Balcázar-Vargas, Jonathan Giddy and G. Oostermeijer 2013-01-10 15:40:36.927 UTC f1d94c22-ea2b-4e64-ae0b-0e669c250d45 2013-01-11 16:58:36.13 UTC SelectYearcensus_data11yearColumnName00initialYears1SelectYears_Interactionyears1years11 In this dialogue automatically comes the years of the census data file, the user selects the first census year. In this example the census was 1987 -1988, therefore the user selects 1987. After this step, the user should confirm the selection. 2012-11-01 14:54:07.608 UTC net.sf.taverna.t2.activitiesinteraction-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.interaction.InteractionActivity years 1 text/plain java.lang.String false years 1 1 http://biovel.googlecode.com/svn/tags/mpm-20130805/select_years.html LocallyPresentedHtml false net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeParseYears_FromCensuyearColumnName0census_data1censusYears11net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSelectYears_InteractionyearsParseYears_FromCensucensusYearsParseYears_FromCensuyearColumnNameyearColumnNameParseYears_FromCensucensus_datacensus_datainitialYearsSelectYears_Interactionyears ca84a3a5-d487-4aed-acb8-69a077a1e2c8 2012-07-13 06:25:21.195 UTC 7e051801-8211-4f67-bebe-d7fd628d68c6 2012-07-13 06:24:48.987 UTC 1066a8b8-63d1-4f5b-aebf-43172f877d3c 2012-09-28 08:44:39.139 UTC b988e318-1a9f-43ed-961d-9995e469a9da 2012-10-17 16:04:45.485 UTC 6f93190c-3845-41bb-ba74-775b9596ae5c 2012-07-11 16:05:56.5 UTC 0556bfe7-45af-4fad-ba6a-50b93e7550f8 2012-10-17 11:47:27.572 UTC 3c1aa296-4541-43a3-8b8a-6448e435e600 2012-07-13 07:51:05.500 UTC 7172c953-a063-4d58-b19d-6912223595e7 2012-10-17 11:47:53.180 UTC fd3b274b-20e6-4335-8eb7-ffccb12c3c66 2012-11-01 14:54:09.368 UTC b033a199-d1e5-4225-b27b-4b828c693d52 2012-07-11 16:06:33.247 UTC be02d73d-ace1-46be-9b88-98048dac2d45 2012-07-11 16:03:25.310 UTC SelectYear 2012-10-17 11:47:50.307 UTC a19224fe-9b73-43b1-a37f-a504d65f9d32 2013-07-29 17:05:10.702 UTC ddf40f01-1f62-4477-a4a4-6467bcc05f25 2012-07-13 07:52:57.834 UTC 001a52e9-fb0b-4c0f-97fc-33bcc3749029 2012-10-17 12:15:53.73 UTC Stage_Matrix_Analysilabel00 Descriptive title for labelling generated outputs. 2012-11-01 12:14:05.640 UTC Gentiana pneumonanthe, Terschelling 2012-10-23 15:39:06.31 UTC shortTermYears00 10 2012-10-29 13:44:13.835 UTC This value will be use to plot a graph that shows a simulation of the number of individuals per stage a few years after the study. This value represents the years of axis X of the output graph: StageVectorPlotShortTerm. 2012-11-01 11:51:21.500 UTC longTermYears00 50 2012-10-29 13:44:37.435 UTC This value contains the maximum number of iterations in the transient dynamic analysis, and hence the total number of years for the long term graphs. The number of years will be used in two output graphs: 1) StageVectorPlotLongTermProportional: the proportion of individuals per stage in the long term. 2) StageVectorPlotLongTermLogarithmic: the number of individuals per stage in the long term. 2012-11-01 11:51:48.46 UTC stages11 [S, J, V, G, D] 2013-06-13 10:54:21.602 UTC Stage input port: 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 2013-06-13 10:54:06.95 UTC stage_matrix11recruited11reproductive11abundances11barPlot0 A bar plot which shows the stable stage distribution (w) of the analysed matrix. In other words, the proportion of individuals per stage. 2012-11-01 12:16:09.906 UTC projectionMatrix0 The stage matrix, displayed with coloured squares to highlight the magnitude of the values. 2012-11-01 10:39:02.187 UTC eigenanalysis_elasticity_matrix0 The elasticity matrix, displayed with coloured squares to highlight the magnitude of the values. 2012-11-01 12:16:26.656 UTC eigenanalysis_sensitivity_matrix_10 A sensitivity matrix showing the sensitivities of the actual transitions (i.e. the sensitivity values of non-zero elements), displayed with coloured squares to highlight the magnitude of the values. 2012-11-01 10:40:25.78 UTC eigenanalysis_sensitivity_matrix_20 A sensitivity matrix showing the sensitivities of all possible transitions (i.e. the sensitivity values of zero and non-zero elements), displayed with coloured squares to highlight the magnitude of the values. 2012-11-01 10:41:08.312 UTC eigenanalysis1 Eigen analysis The Eigen analysis results are a set of demographic statistics: Lambda 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.: λ = 1 (population is stable), λ > 1 (population is growing) and finally λ < 1 (population is decreasing). The stable stage distribution (w): It is the proportion of the number of individuals per stage. It is given analytically by the right eigenvector (another property of the transition matrix) that corresponds to the dominant eigenvalue Elasticity and Sensitivity: Sensitivity and elasticity analyses are prospective analyses. The sensitivity matrix: The sensitivity gives the effect on λ of changes in any entry of the matrix, including those that may, at a given context, be regarded as fixed at zero or some other value. The derivative tells what would happened to λ if aij was to change, not whether, or in what direction, or how much, aij actually change. The hypothetical results of such impossible perturbations may or may not be of interest, but they are not zero. It is up to you to decide whether they are useful (Caswell 2001). When comparing the λ-sensitivity values for all matrix elements one can find out in what element a certain increase has the biggest impact on λ. However, a 0.01 increase in a survival matrix element is hard to compare to a 0.01 increase in a reproduction matrix element, because the latter is not bound between 0 and 1 and can sometimes take high values. Increasing matrix element a14 (number of S (seedlings) the next year produced by a G (Reproductive individuals)) with 0.01 from 7.666 to 7.676 does not have a noticeable effect on λ. For comparison between matrix elements it can therefore be more insightful to look at the impact of proportional changes in elements: by what percentage does λ change if a matrix element is changed by a certain percentage? This proportional sensitivity is termed elasticity (Description based on Oostermeijer data, based on Jongejans & de Kroon 2012). The Elasticity matrix: The elasticities of λ with respect to the stage are often interpreted as the “contributions” of each of the stages to λ. This interpretation relies on the demonstration, by de Kroon et al (1986) that the elasticitis of the λ with respect to the stage, always sum to 1. For further information see: de Kroon, et al., 1986. and Caswell 2001. Reproductive value (v): scaled so v[1]=1. To what extent will a plant or animal of a determinate category or stage , contribute to the ancestry of future generation. The damping ratio: it can be considered as a measure of the intrinsic resilience of the population, describing how quickly transient dynamics decay following disturbance or perturbation regardless of population structure, the larger the p, the quicker the population converges. 2012-11-01 12:16:19.0 UTC $lambda1 [1] 1,232338 $stable.stage S J V G D 0,14218794 0,16166957 0,65944861 0,02285525 0,01383863 $sensitivities S J V G D S 0 0 0 0,006042076 0 J 0,14255579 0,1620878 0 0,02291438 0 V 0,08206359 0,0933074 0,3806 0,01319088 0,00798695 G 0 2,7675986 11,28901 0,391255815 0,2369016 D 0 0,3325676 1,35654 0 0,02846721 $elasticities S J V G D S 0 0 0 0,037589187 0 J 0,006706037 0,001315287 0 0,15406651 0 V 0,03088315 0,062844075 0,27823767 0,003058271 0,005576792 G 0 0,089832455 0,08252831 0,196541848 0,022353201 D 0 0,008096016 0,01983398 0 0,000537213 $repro.value S J V G D 1 3,792468 2,18317 64,755197 7,781288 $damping.ratio [1] 2,0902 2012-10-31 12:11:52.131 UTC stageVectorPlotShortTerm0 A plot that charts the number of individuals per stage vs. years in the short-term (e.g. 5 or 10 years). The number of years is related to the short-term years input value. 2012-11-01 11:47:06.281 UTC stageVectorPlotLongTermLogarithmic0 The number of individuals per stage in the long term 2012-11-01 11:46:16.968 UTC populationProjection0 Population projection: Lambda or dominant eigenvalue: The population will be stable, grow or decrease at a rate given by lambda: e.g.: λ = 1 (population is stable), λ > 1 (population is growing) and finally λ < 1 (population is decreasing). e.g. The projected population growth rate (λ) is 1.237, meaning that the population is projected to increase with 23% per year if these model parameters remain unchanged. The stable stage distribution (stable.stage): It is the proportion of the number of individuals per stage and it is given by (w). Stage vector (stage.vectors): it is the projection of the number of individuals per category per year in the long term, the long term was stipulated in the input port (long term years) (e.g. 50 years). Population sizes (pop.sizes): it is the total population size per year in the long-term (e.g. 50 years). Population change (pop.changes): are the lambda values per year in the long-term (e.g. 50 years). 2012-11-01 12:14:44.46 UTC $lambda [1] 1.232338 $stable.stage S J V G D 0.14218794 0.16166957 0.65944861 0.02285525 0.01383863 $stage.vectors 0 1 2 3 4 5 6 7 S 69 161 176.33333 187.22072 219.09046 261.92397 318.22637 389.44517 J 100 179 201.69476 214.57709 249.78014 298.27181 362.08839 442.95984 V 111 258 467.40332 685.98643 903.75302 1149.34660 1437.18683 1783.39662 G 21 23 24.42009 28.57702 34.16400 41.50779 50.79720 62.39105 D 43 6 10.15818 14.70876 19.13949 24.22235 30.22041 37.46071 8 9 10 11 12 13 14 S 478.33138 588.52367 724.70467 892.75361 1099.9811 1355.4346 1670.2865 J 543.96054 669.21317 824.03064 1015.09148 1250.7042 1541.1537 1899.1418 V 2204.99218 2721.56781 3356.41003 4137.71650 5099.9406 6285.3667 7746.0003 G 76.76396 94.52670 116.44612 143.47580 176.7958 217.8635 268.4762 D 46.29325 57.12499 70.44214 86.83496 107.0256 131.9009 162.5519 15 16 17 18 19 20 21 S 2058.3178 2536.5199 3125.836 3852.0783 4747.0575 5849.9764 7209.1464 J 2340.3370 2884.0582 3554.118 4379.8647 5397.4679 6651.5012 8196.8954 V 9545.8697 11763.8434 14497.093 17865.3551 22016.1771 27131.3836 33435.0416 G 330.8504 407.7177 502.445 619.1814 763.0404 940.3234 1158.7961 D 200.3221 246.8664 304.224 374.9074 462.0130 569.3564 701.6396 22 23 24 25 26 27 28 S 8884.1038 10948.218 13491.904 16626.585 20489.572 25250.078 31116.629 J 10101.3442 12448.269 15340.474 18904.649 23296.916 28709.674 35380.022 V 41203.2756 50776.363 62573.642 77111.875 95027.892 117106.479 144314.761 G 1428.0284 1759.814 2168.685 2672.553 3293.488 4058.691 5001.679 D 864.6572 1065.550 1313.118 1618.205 1994.175 2457.498 3028.468 29 30 31 32 33 34 35 S 38346.204 47255.483 58234.725 71764.863 88438.565 108986.20 134307.83 J 43600.144 53730.113 66213.658 81597.604 100555.825 123918.76 152709.79 V 177844.559 219164.602 270084.859 332835.826 410166.224 505463.41 622901.75 G 6163.759 7595.834 9360.634 11535.465 14215.591 17518.41 21588.61 D 3732.096 4599.204 5667.773 6984.612 8607.403 10607.23 13071.69 36 37 38 39 40 41 42 S 165512.64 203967.51 251356.91 309756.66 381724.90 470414.08 579709.13 J 188190.08 231913.78 285796.15 352197.45 434026.28 534867.07 659136.99 V 767625.48 945974.02 1165759.69 1436609.93 1770388.96 2181717.52 2688613.33 G 26604.46 32785.68 40403.04 49790.20 61358.36 75614.23 93182.29 D 16108.74 19851.41 24463.65 30147.49 37151.89 45783.69 56420.97 43 44 45 46 47 48 49 S 714397.57 880379.2 1084924.8 1336994.0 1647628.4 2030435.1 2502182.2 J 812279.54 1001002.9 1233573.9 1520179.9 1873375.5 2308631.7 2845014.5 V 3313280.28 4083081.1 5031735.8 6200799.1 7641480.1 9416886.1 11604786.2 G 114832.08 141511.9 174390.5 214908.1 264839.4 326371.6 402200.1 D 69529.71 85684.1 105591.8 130124.7 160357.7 197614.8 243528.3 $pop.sizes [1] 3.440000e+02 6.270000e+02 8.800097e+02 1.131070e+03 1.425927e+03 [6] 1.775273e+03 2.198519e+03 2.715653e+03 3.350341e+03 4.130956e+03 [11] 5.092034e+03 6.275872e+03 7.734447e+03 9.531719e+03 1.174646e+04 [16] 1.447570e+04 1.783901e+04 2.198372e+04 2.709139e+04 3.338576e+04 [21] 4.114254e+04 5.070152e+04 6.248141e+04 7.699821e+04 9.488782e+04 [26] 1.169339e+05 1.441020e+05 1.775824e+05 2.188416e+05 2.696868e+05 [31] 3.323452e+05 4.095616e+05 5.047184e+05 6.219836e+05 7.664940e+05 [36] 9.445797e+05 1.164041e+06 1.434492e+06 1.767779e+06 2.178502e+06 [41] 2.684650e+06 3.308397e+06 4.077063e+06 5.024319e+06 6.191659e+06 [46] 7.630217e+06 9.403006e+06 1.158768e+07 1.427994e+07 1.759771e+07 $pop.changes [1] 1.822674 1.403524 1.285293 1.260689 1.244995 1.238412 1.235219 1.233715 [9] 1.232996 1.232652 1.232488 1.232410 1.232372 1.232354 1.232346 1.232342 [17] 1.232340 1.232339 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 [25] 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 [33] 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 [41] 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 1.232338 [49] 1.232338 2012-11-01 11:42:50.109 UTC stageVectorPlotLongTermProportional0 Stage vector plot long term proportional: Is the proportion of individuals per stage in the long term (e.g.: 50 years) 2012-11-01 11:45:40.640 UTC dampingRatio0 2.0901. 2012-11-01 11:44:15.62 UTC Damping ratio: The ratio between the dominant eigenvalue and the second highest eigenvalue of a transition matrix is called the damping ratio, and it can be considered as a measure of the intrinsic resilience of the population, describing how quickly transient dynamics decay following disturbance or perturbation regardless of population structure, (the larger the damping ratio, the quicker the population converges). High damping ratios tell you that the dominant stable stage distribution is reached fairly soon. 2012-11-01 12:14:56.312 UTC fundamentalMatrix1 Age specific survival The fundamental matrix workflow gives the basic information on age-specific survival, this includes the mean, variance and coefficient of variation (cv) of the time spent in each stage class and the mean and variance of the time to death. Fundamental matrix (N): is the mean of the time spent in each stage class. e.g.: For our Gentiana example means a J plants will spends, on average, about 1 year as a Juvenile, 11 as vegetative plant, less than a year a reproductive and dormant plant. Variance (var): is the variance in the amount of time spent in each stage class. Coefficient of variation (cv): is the coefficient of variation of the time spent in each class (sd/mean- the ratio of the standard deviation to the mean). Meaneta: is the mean of time to death, of life expectancy of each stage. e.g. The mean age at death is the life expectancy; the life expectancy of a new individual seedling is 8 years. Vareta: is the variance of time to death. 2012-11-01 10:47:32.328 UTC $N S J V G D S 1.00000000 0.0000000 0.0000000 0.0000000 0.0000000 J 0.05855658 1.0101010 0.0000000 0.0000000 0.0000000 V 6.89055876 11.9968091 13.3581662 10.0186246 12.9606017 G 0.20844800 0.4667882 0.3911175 2.9183381 0.6919771 D 0.12890879 0.2523299 0.2464183 0.1848138 1.2628940 $var S J V G D S 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 J 0.05631067 0.01020304 0.0000000 0.0000000 0.0000000 V 129.72009930 164.59050165 165.0824377 157.2694415 165.3219447 G 0.96474493 2.03981226 1.7387357 5.5983592 2.8680368 D 0.18007000 0.32133154 0.3152601 0.2478305 0.3320072 $cv S J V G D S 0.000000 NaN NaN NaN NaN J 4.052468 0.100000 NaN NaN NaN V 1.652910 1.069391 0.9618417 1.2517398 0.9920649 G 4.712035 3.059674 3.3713944 0.8107646 2.4473757 D 3.291836 2.246508 2.2785654 2.6936618 0.4562542 $meaneta S J V G D 8.286472 13.726028 13.995702 13.121777 14.915473 $vareta S J V G D 143.4207 181.1844 181.6537 177.2333 181.2319 2012-11-01 11:37:55.718 UTC generationTime0 Generation time: The time T required for the population to increase by a factor of Ro (net reproductive rate). e.g. for Generation time T and Net reproductive rate Ro: If T = 8.13 and Ro = 5.46 then the average individual in the study year replaced itself with about five new plants and took approximately 8 years to do so. 2012-11-01 12:15:30.218 UTC 8.1302 2012-10-31 12:12:54.819 UTC netReproductiveRate0 5.4657 2012-10-31 12:13:22.678 UTC The net reproductive rate is the mean number of offspring by which a new-born individual will be replaced by the end of its life, and thus the rate by which the population increases from one generation to the next. 2012-11-01 11:40:25.562 UTC survivalCurvePlot0sensitivity_matrix1sensitivityPlot0elasticity_matrix1ElasticityPlot0cohenCumulative0keyfitzDelta0Eigen_analysisplotTitle0speciesName0stage_matrix1projectionMatrix00sensitivityMatrix100sensitivityMatrix200elasticityMatrix00barPlot00eigenanalysis11 Eigen analysis of a stage matrix model. This component of the workflow performs the Eigen analysis. This analysis results are a set of demographic statistics: Lambda or dominant eigenvalue (λ), the stable stage distribution, sensitivity and elasticity matrix, reproductive value and damping ratio (see outputs: Eigen_analysis). 2012-11-01 10:11:39.406 UTC net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeTransientDynamicsplot_title0short_term_years0long_term_years0abundances1stage_matrix1stage_vector_plot_short_term00stage_vector_plot_long_term_logarithmic00pop_projection00stage_vector_plot_long_term_proportional00damping_ratio00 Transient Dynamics: This workflow produces plots of the short-term dynamics and convergence to stable stage distribution using stage vector projections (see outputs: Transient Dynamics). 2012-11-01 10:13:40.390 UTC net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeFecundityRows_FromRecruitedStagesall_values1some_values1indices11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.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-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeFecundityCols_FromReproductiveStagesall_values1some_values1indices11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.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-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeFundamental_matrixcolumns_fecundity1rows_fecundity1stage_matrix1fundamental_matrix11 Age specific survival or fundamental analysis. This workflow gives the basic information on age-specific survival, this includes the fundamental matrix (N): is the mean of the time spent in each stage class, the Variance (var): is the variance in the amount of time spent in each stage class, the Coefficient of variation (cv): is the coefficient of variation of the time spent in each class (sd/mean- the ratio of the standard deviation to the mean), Meaneta: is the mean of time to death, of life expectancy of each stage and Vareta: is the variance of time to death. (see outputs: Age specific survival). 2012-11-01 10:11:32.593 UTC net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeGeneration_time__T_columns_fecundity1rows_fecundity1stage_matrix1generation_time00 Generation time (T): This component of the workflow calculates the generation time. The time T required for the population to increase by a factor of Ro (net reproductive rate). In other words, T calculates how much time takes to a plant/animal to replace itself by a factor of Ro (see outputs: Generation time). 2012-11-01 10:12:51.109 UTC net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeNet_reproductive_ratcolumns_fecundity1rows_fecundity1stage_matrix1netReproductiveRate00 Net reproductive rate (Ro): This section of the workflow calculates the net reproductive rate (Ro). Ro is the mean number of offspring by which a new-born individual will be replaced by the end of its life, and thus the rate by which the population increases from one generation to the next (see outputs: Net reproductive rate). 2012-11-01 10:12:20.156 UTC net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSurvival_CurvefecundityRows1fecundityCols1plotTitle0stageMatrix1survivalCurvePlot00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeSensitivity_and_Elasidentifier0stage_matrix1sensitivityPlot00elasticityPlot00sensitivity_matrix11elasticity_matrix11net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeCohen_Cumulativeabundances1stage_matrix1cohenCumulative00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeKeyfitz_Deltaabundances1stage_matrix1keyfitzDelta00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeEigen_analysisplotTitlelabelEigen_analysisspeciesNamelabelEigen_analysisstage_matrixstage_matrixTransientDynamicsplot_titlelabelTransientDynamicsshort_term_yearsshortTermYearsTransientDynamicslong_term_yearslongTermYearsTransientDynamicsabundancesabundancesTransientDynamicsstage_matrixstage_matrixFecundityRows_FromRecruitedStagesall_valuesstagesFecundityRows_FromRecruitedStagessome_valuesrecruitedFecundityCols_FromReproductiveStagesall_valuesstagesFecundityCols_FromReproductiveStagessome_valuesreproductiveFundamental_matrixcolumns_fecundityFecundityCols_FromReproductiveStagesindicesFundamental_matrixrows_fecundityFecundityRows_FromRecruitedStagesindicesFundamental_matrixstage_matrixstage_matrixGeneration_time__T_columns_fecundityFecundityCols_FromReproductiveStagesindicesGeneration_time__T_rows_fecundityFecundityRows_FromRecruitedStagesindicesGeneration_time__T_stage_matrixstage_matrixNet_reproductive_ratcolumns_fecundityFecundityCols_FromReproductiveStagesindicesNet_reproductive_ratrows_fecundityFecundityRows_FromRecruitedStagesindicesNet_reproductive_ratstage_matrixstage_matrixSurvival_CurvefecundityRowsFecundityRows_FromRecruitedStagesindicesSurvival_CurvefecundityColsFecundityCols_FromReproductiveStagesindicesSurvival_CurveplotTitlelabelSurvival_CurvestageMatrixstage_matrixSensitivity_and_ElasidentifierlabelSensitivity_and_Elasstage_matrixstage_matrixCohen_CumulativeabundancesabundancesCohen_Cumulativestage_matrixstage_matrixKeyfitz_DeltaabundancesabundancesKeyfitz_Deltastage_matrixstage_matrixbarPlotEigen_analysisbarPlotprojectionMatrixEigen_analysisprojectionMatrixeigenanalysis_elasticity_matrixEigen_analysiselasticityMatrixeigenanalysis_sensitivity_matrix_1Eigen_analysissensitivityMatrix1eigenanalysis_sensitivity_matrix_2Eigen_analysissensitivityMatrix2eigenanalysisEigen_analysiseigenanalysisstageVectorPlotShortTermTransientDynamicsstage_vector_plot_short_termstageVectorPlotLongTermLogarithmicTransientDynamicsstage_vector_plot_long_term_logarithmicpopulationProjectionTransientDynamicspop_projectionstageVectorPlotLongTermProportionalTransientDynamicsstage_vector_plot_long_term_proportionaldampingRatioTransientDynamicsdamping_ratiofundamentalMatrixFundamental_matrixfundamental_matrixgenerationTimeGeneration_time__T_generation_timenetReproductiveRateNet_reproductive_ratnetReproductiveRatesurvivalCurvePlotSurvival_CurvesurvivalCurvePlotsensitivity_matrixSensitivity_and_Elassensitivity_matrixsensitivityPlotSensitivity_and_ElassensitivityPlotelasticity_matrixSensitivity_and_Elaselasticity_matrixElasticityPlotSensitivity_and_ElaselasticityPlotcohenCumulativeCohen_CumulativecohenCumulativekeyfitzDeltaKeyfitz_DeltakeyfitzDelta d98aaf13-21d0-487f-bb75-eec4eaf390a7 2012-10-29 13:45:38.787 UTC b740ea7f-7c1a-4ae3-b632-e22a0879bd5c 2012-10-12 09:21:33.957 UTC 543f4718-3a39-4813-b1f8-b8173435abc1 2012-10-10 15:38:22.120 UTC 6e4e9dd0-334b-403a-9632-8423f9636fe7 2012-07-11 13:00:55.837 UTC 139c0c49-10fe-4b19-b53a-203fea79943e 2013-06-21 09:01:06.377 UTC ac715f0e-f99c-46df-bfe6-0c70a550bb61 2012-11-01 11:48:54.375 UTC Maria Paula Balcázar-Vargas, Jonathan Giddy and Gerard Oostermeijer 2012-10-30 15:58:04.755 UTC 27fa10ea-ca41-4311-b50c-eb7e2fe66489 2012-07-11 15:49:21.753 UTC 6315c9ba-381a-44ce-82cd-ea6f5aae35d5 2012-07-13 09:27:13.462 UTC 51e48dec-1225-494a-a91d-3e17eb05f5c9 2012-11-26 15:38:34.136 UTC 9c7eab3e-170b-4f26-bb3e-bdc22eecec62 2012-11-01 11:51:49.687 UTC 1144714e-3e7e-46d7-817a-80f25646e7e0 2012-10-17 12:17:52.970 UTC ada91081-1164-46f7-9ae2-1b1a9492fc4a 2013-06-13 10:54:26.885 UTC 78c30687-4ce1-405e-a822-f624fef969b9 2012-10-30 10:40:44.746 UTC e0424e01-ed30-4c94-95b8-143751fca7a2 2012-07-11 16:07:29.317 UTC 12eeeead-ffb3-4fcd-944c-efcb7de6a5fd 2012-10-18 10:18:38.541 UTC de650b9e-a38c-4765-9e8f-a8b142903717 2013-06-21 11:08:53.848 UTC e6fb6b0c-e37d-4469-ac93-c620d440011d 2013-06-24 09:44:31.552 UTC bf460b28-85d7-4bbd-bd14-a89dabcc1eb7 2012-11-01 11:56:17.546 UTC 4753d12c-1f1d-4454-97fe-6a2dcecc38b6 2012-07-13 07:45:12.849 UTC 214b1114-efbc-4d54-bb20-4fa1f9beb89f 2012-09-20 13:37:17.940 UTC f7125a55-d200-4c9f-9806-6b05549b428a 2012-10-29 13:42:50.131 UTC b43beb73-dc0f-46e8-955e-a812ec1eb70f 2012-11-21 15:48:35.252 UTC Stage Matrix Analysis 2013-06-24 09:44:29.102 UTC abbcd7d0-d152-4988-a1fc-d5149f19410c 2012-11-01 10:49:50.46 UTC bd483775-9c66-42a8-bf44-153f086ae7ba 2012-10-26 10:38:50.743 UTC d7a4168c-96b3-4d07-8cd9-20e70968f5e6 2013-06-21 16:23:59.308 UTC 34d1cf2c-a02a-4b6e-b39d-b6f55da1a025 2012-07-11 20:58:04.370 UTC The Matrix Population Models Workflow provides an environment to perform several analyses on a stage-matrix with no density dependence: - Eigen analysis; - Age specific survival; - Generation time (T); - Net reproductive rate (Ro); - Transient Dynamics; - Bootstrap of observed census transitions (Confidence intervals of λ). 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 using and based on Package ‘popbio’ in R. (Stubben & Milligan 2007; Stubben, Milligan & Nantel 2011). Literature ========== Caswell, H. 1986. Life cycle models for plants. Lectures on Mathematics in the Life Sciences 18: 171-233. Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts. de Kroon, H. J., A. Plaiser, J. van Groenendael, and H. Caswell. 1986. Elasticity: The relative contribution of demographic parameters to population growth rate. Ecology 67: 1427-1431. Horvitz, C., D.W. Schemske, and Hal Caswell. 1997. The relative "importance" of life-history stages to population growth: Prospective and retrospective analyses. In S. Tuljapurkar and H. Caswell. Structured population models in terrestrial and freshwater systems. Chapman and Hall, New York. Jongejans E. & H. de Kroon. 2012. Matrix models. Chapter in Encyclopaedia of Theoretical Ecology (eds. Hastings A & Gross L) University of California, p415-423 Mesterton-Gibbons, M. 1993. Why demographic elasticities sum to one: A postscript to de Kroon et al. Ecology 74: 2467-2468. 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., S. Townley and D.J. Hodgson 2011. A framework for studying transient dynamics of population projection matrix models. Ecology Letters 14: 959–970 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 van Groenendael, J., H. de Kroon, S. Kalisz, and S. Tuljapurkar. 1994. Loop analysis: Evaluating life history pathways in population projection matrices. Ecology 75: 2410-2415. 2013-02-12 09:16:55.62 UTC f8683e08-b7e0-4a1b-a119-e977d6ba23e5 2012-12-09 23:15:12.16 UTC 3618d570-4189-464f-8921-ae27391d6512 2012-10-18 11:17:27.472 UTC 6c9032a6-6f3a-43fc-85f7-4a65f24bdfa0 2012-10-30 11:05:28.590 UTC 8b100d25-27a9-4e50-893b-27ca7f42a4e2 2012-10-23 12:02:36.167 UTC e8da845b-093d-4f3c-9e1c-8dbcfdb703fb 2012-10-17 15:44:05.374 UTC 6317b32f-0f55-4e22-9c4b-72bbe80796ca 2012-10-17 11:48:27.846 UTC e491d0ce-ea60-47c4-94f0-cb8f923433da 2012-10-23 15:41:09.199 UTC a6f209c5-1062-4064-b41a-91caff11da78 2013-06-10 15:21:06.432 UTC 477d4f86-74ce-422e-bfb7-6a1057a43f3d 2012-10-23 16:07:32.793 UTC 485eeece-ff3e-4ab6-92ac-8a707279130b 2012-11-26 16:08:28.274 UTC 290bff82-b044-4475-9f8a-65e5a8ee3c3b 2012-11-01 11:51:22.265 UTC 31de0f25-8b52-4b7c-a868-4eb26381b768 2012-07-11 16:08:08.989 UTC 28cbe7df-43ca-4479-a886-649875ad33df 2012-10-30 15:59:46.983 UTC 41d22268-b28d-41d6-a62a-2bd6c9588ef8 2012-11-01 11:39:57.328 UTC 0c6d94db-6fd7-440a-a306-99b63cd378e7 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ac5a0e53-1ce5-48c4-aa7f-857d0185c16f 2012-10-26 10:36:36.257 UTC 9159d5c8-8084-403e-9941-37a0babea12d 2012-07-13 08:10:15.746 UTC 5bdb4704-9322-4cbc-8d08-4b87aa1f2318 2012-07-11 22:49:36.115 UTC 6addddd1-bdf4-4f01-a226-aed869898b46 2012-10-23 15:39:43.253 UTC d63e1001-8c5e-4619-88e8-829e59b157c4 2012-09-28 08:44:45.610 UTC d2e98a62-d554-4654-a359-39eaa6c9c9d5 2012-09-20 13:35:32.442 UTC b0e4235a-5fe4-4228-a361-9dd543d8ec95 2012-09-19 15:33:22.278 UTC c8f2d1e0-de4e-44c9-a758-efca43706d64 2012-11-01 10:26:06.156 UTC e9b5e5d2-9871-4eba-b7a1-0635c8fc1bf5 2012-07-13 09:10:07.45 UTC d64b5ae1-a5ad-4260-89b2-9822403cfc28 2012-10-17 14:53:12.993 UTC ff5bc61b-0d13-4622-adc8-2aedd7b7038c 2012-07-13 09:20:52.134 UTC 745cb63a-e27a-4cbf-8fc0-5a1be24ed0ac 2012-11-01 10:17:46.562 UTC 6933ad38-f960-4f05-ae40-0352c2a97723 2012-07-11 16:13:31.963 UTC 6bf5fc1d-7f9a-48a3-8b77-b4cf27f2b52a 2012-10-17 14:47:18.255 UTC 1378f96b-c536-4968-a27f-2c7956207e6f 2012-10-12 09:24:28.167 UTC 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42e22b26-02a1-413f-99ef-dfa978e86c1f 2012-10-23 14:48:02.159 UTC aab9fd22-7f16-41d7-a280-1f426ec85097 2012-10-23 15:42:20.102 UTC 0524104c-efee-44ad-a088-f428caefeb8d 2012-10-30 10:41:29.116 UTC 2d852a79-7b62-40cc-a176-aadadbfc2c03 2012-10-17 14:40:53.717 UTC ba0a12e6-e2cc-428f-b901-978b4d2e8320 2012-10-18 11:26:48.0 UTC a23877ec-cc0a-43f7-958e-eb68ab303cb8 2013-06-10 15:15:29.501 UTC e9fc8f5f-13bf-432d-875e-3c5858b128d0 2012-12-10 16:26:39.762 UTC e05732a8-9627-4bfa-b871-d8d4a9c53fb6 2012-07-11 20:55:29.517 UTC acb06188-582e-4ece-91ab-034e55a57158 2012-10-23 15:39:06.238 UTC 6a6df203-47b2-4b26-855f-ff8c48eed2bc 2012-09-19 15:38:42.701 UTC 199c86f0-df6a-425c-b4dd-6aa852bcbff5 2012-07-13 09:03:22.271 UTC e90b3be2-6a1c-40c2-8fa9-4c933a35d03d 2013-06-21 08:57:54.390 UTC ace5fe46-77ef-4a86-b67b-bde22c741ac8 2012-07-13 09:26:09.444 UTC a8004bfd-bcb7-4f8f-986c-a4c03dc24ca7 2013-02-12 09:16:55.687 UTC d0b4659e-61e2-4b62-85f5-726a2f09105c 2013-06-21 09:30:22.764 UTC f0b240a1-0585-4dd9-937b-560ed8f60bad 2012-10-26 11:24:13.754 UTC 87f752c3-6599-4607-8858-76f668ae65d2 2012-11-01 10:43:10.78 UTC 85c44158-b0f7-4e74-a6d7-b2d0b0093cc2 2012-10-31 12:14:30.69 UTC ca39ee21-5bd7-4068-8fcf-f69199409104 2012-10-23 15:44:47.549 UTC 69d11822-5bf3-40d1-a6ce-cc0110a366dd 2012-07-13 08:06:33.163 UTC StageMatrixFromCensucensus_data11stage_matrix2initialYears1matrixStages1reproductiveStages1recruitedStages1report1GetStagesstageColumnHeader0census_data1matrixStages11recruitedStages11reproductiveStages11net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokestagevalue00net.sf.taverna.t2.activitiesstringconstant-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.stringconstant.StringConstantActivity stage net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeGetInitialYearyearColumnName0census_data1initialYears11net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Invokeyearvalue00net.sf.taverna.t2.activitiesstringconstant-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.stringconstant.StringConstantActivity year net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeStageMatrix_FromCensstages1initialYear0recruitedStages1census_data1stage_matrix11report00net.sf.taverna.t2.activitiesdataflow-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.dataflow.DataflowActivitynet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeGetStagesstageColumnHeaderstagevalueGetStagescensus_datacensus_dataGetInitialYearyearColumnNameyearvalueGetInitialYearcensus_datacensus_dataStageMatrix_FromCensstagesGetStagesmatrixStagesStageMatrix_FromCensinitialYearGetInitialYearinitialYearsStageMatrix_FromCensrecruitedStagesGetStagesrecruitedStagesStageMatrix_FromCenscensus_datacensus_datastage_matrixStageMatrix_FromCensstage_matrixinitialYearsGetInitialYearinitialYearsmatrixStagesGetStagesmatrixStagesreproductiveStagesGetStagesreproductiveStagesrecruitedStagesGetStagesrecruitedStagesreportStageMatrix_FromCensreport a2cee785-18ef-4208-8677-b37970b920a7 2012-07-13 07:51:10.754 UTC 6317b32f-0f55-4e22-9c4b-72bbe80796ca 2012-10-17 11:48:27.846 UTC 09aa9ebd-4e1d-4579-83f6-727da90eae35 2012-09-20 13:38:43.850 UTC 3b844874-3fc8-4b62-9f02-5c86ae0e053d 2012-07-13 09:02:25.256 UTC 67c8362b-27e4-4d58-9326-77a7e2440163 2012-10-17 13:24:04.125 UTC 2615d90a-7a5e-464e-89b6-20fec7bc1014 2012-07-13 09:11:19.100 UTC 5a015f30-86bc-42d9-9d98-ed27e8f92d44 2012-10-18 11:26:43.420 UTC 9fa97e87-bd43-4312-a36f-8ac512988b80 2012-07-13 06:25:39.0 UTC e9b5e5d2-9871-4eba-b7a1-0635c8fc1bf5 2012-07-13 09:10:07.45 UTC 3f094d6e-7d4f-43ea-a672-8f56b7e9b327 2012-09-19 15:54:02.713 UTC ecef24cd-4e05-4e89-b47d-2627857c98bc 2012-07-13 09:06:31.770 UTC 6a6df203-47b2-4b26-855f-ff8c48eed2bc 2012-09-19 15:38:42.701 UTC 69d11822-5bf3-40d1-a6ce-cc0110a366dd 2012-07-13 08:06:33.163 UTC 1144714e-3e7e-46d7-817a-80f25646e7e0 2012-10-17 12:17:52.970 UTC StageMatrixFromCensusData_Interactive 2012-10-17 14:34:10.90 UTC 03a6518b-b394-4f3d-a6f8-cbc681d5b0c8 2012-10-17 14:34:13.815 UTC 6315c9ba-381a-44ce-82cd-ea6f5aae35d5 2012-07-13 09:27:13.462 UTC 61e17b4e-2110-470a-8aa8-db97ea3341e0 2012-07-11 15:46:57.885 UTC 5bdb4704-9322-4cbc-8d08-4b87aa1f2318 2012-07-11 22:49:36.115 UTC cc5b393c-818f-4016-a1d8-890885b584d3 2012-07-13 09:00:07.232 UTC 8a185a1f-a8b9-43f0-8c16-c2d11e3bb15c 2012-11-01 14:54:12.138 UTC 95e5f4d5-c433-4be8-852f-c338eac52049 2012-10-08 10:37:08.148 UTC f9d69679-5e04-46e4-b77b-c70d19cb4c0d 2012-07-13 08:51:15.886 UTC 34d1cf2c-a02a-4b6e-b39d-b6f55da1a025 2012-07-11 20:58:04.370 UTC b740ea7f-7c1a-4ae3-b632-e22a0879bd5c 2012-10-12 09:21:33.957 UTC 4988e019-bf93-40bd-b095-6a33aec7e968 2012-07-13 08:39:49.679 UTC 3652cbd1-72c4-47e1-9479-ddedb5f81d98 2012-07-13 09:07:01.485 UTC 9cc9920c-7d50-4c5b-9800-20cc1cfd72cf 2012-09-20 14:54:19.489 UTC 9911f697-7c3a-4c84-9f68-dcd18fa5f907 2012-10-10 16:16:36.407 UTC 18e99b4e-e6d1-4c87-86cb-f4b3f40fec87 2012-07-13 08:43:47.651 UTC e0424e01-ed30-4c94-95b8-143751fca7a2 2012-07-11 16:07:29.317 UTC 59c1f00e-85e1-4548-a1e9-32fe56dc0de5 2012-10-16 15:21:54.632 UTC e8d180a6-7a5c-4421-b788-db26f3589ae8 2012-09-20 13:43:51.734 UTC d6d1403b-6c66-4f00-a6c8-3440a4446d36 2012-10-17 12:24:40.99 UTC b5ac108a-6c87-47dd-9d30-913993d050c1 2012-10-18 11:17:13.194 UTC d2e98a62-d554-4654-a359-39eaa6c9c9d5 2012-09-20 13:35:32.442 UTC a6efda97-4c1a-4ce4-9b65-6a87f8d11e78 2012-07-13 09:14:16.348 UTC 53b6584e-7b75-48ac-976b-11154cef701f 2012-07-13 09:04:05.440 UTC 543f4718-3a39-4813-b1f8-b8173435abc1 2012-10-10 15:38:22.120 UTC 27fa10ea-ca41-4311-b50c-eb7e2fe66489 2012-07-11 15:49:21.753 UTC ff5bc61b-0d13-4622-adc8-2aedd7b7038c 2012-07-13 09:20:52.134 UTC 63a2b448-4635-443c-97d8-b4251aebf074 2012-10-17 16:04:57.224 UTC bef35e44-1127-458d-b2ee-61048bd64a07 2012-07-13 06:22:03.734 UTC 11db6475-ebfc-492f-af7d-3a4185d279ce 2012-09-19 15:49:14.786 UTC 72284f53-d320-410c-aa28-752f959d28ce 2012-10-17 13:12:03.770 UTC 40bc579b-5ec9-4b69-b26c-4f601b3bb0c9 2012-10-20 06:47:45.515 UTC 214b1114-efbc-4d54-bb20-4fa1f9beb89f 2012-09-20 13:37:17.940 UTC f83a15fc-a58e-4579-b134-e2f6b6a2c17d 2012-10-17 13:25:15.826 UTC e05732a8-9627-4bfa-b871-d8d4a9c53fb6 2012-07-11 20:55:29.517 UTC 6e4e9dd0-334b-403a-9632-8423f9636fe7 2012-07-11 13:00:55.837 UTC 199c86f0-df6a-425c-b4dd-6aa852bcbff5 2012-07-13 09:03:22.271 UTC 1426c3c1-f38e-452e-8b2a-d045c56296e1 2012-10-16 15:15:35.841 UTC 6933ad38-f960-4f05-ae40-0352c2a97723 2012-07-11 16:13:31.963 UTC b0e4235a-5fe4-4228-a361-9dd543d8ec95 2012-09-19 15:33:22.278 UTC 5639ee4e-2035-4cc9-911f-fb435d58a930 2012-10-07 22:14:42.810 UTC d63e1001-8c5e-4619-88e8-829e59b157c4 2012-09-28 08:44:45.610 UTC 4753d12c-1f1d-4454-97fe-6a2dcecc38b6 2012-07-13 07:45:12.849 UTC 3e775ac4-0bf5-435e-979e-26753958554c 2012-07-13 06:22:29.620 UTC d876c281-ea57-4da0-a470-f3bfbd4c7ad4 2012-07-11 13:05:24.619 UTC e8d2435e-c0dc-4d7a-86a6-3aaa6fbf5b60 2012-10-08 10:18:15.958 UTC 21662d80-f207-4a54-9844-8f24acb91a27 2012-07-13 09:22:22.131 UTC 48a5143e-e914-4c23-9444-3747ca288582 2013-07-30 05:45:12.637 UTC 38b586a2-f222-4a9e-abc3-86f642f00ae1 2012-10-30 09:57:01.745 UTC 9159d5c8-8084-403e-9941-37a0babea12d 2012-07-13 08:10:15.746 UTC 9acbe7fa-78da-4dd1-9620-7d52c5a1ebad 2012-10-17 12:23:36.745 UTC 31de0f25-8b52-4b7c-a868-4eb26381b768 2012-07-11 16:08:08.989 UTC 763a56f3-277f-409b-9741-0b0a868ceb22 2013-07-29 17:05:14.150 UTC ace5fe46-77ef-4a86-b67b-bde22c741ac8 2012-07-13 09:26:09.444 UTC 104a6f4f-33c3-4b8c-b4d7-55ac8fa330f7 2012-07-13 07:53:21.635 UTC dd908533-9aaa-4170-9698-b7c7cdf85fe0 2012-10-20 06:47:28.279 UTC 1378f96b-c536-4968-a27f-2c7956207e6f 2012-10-12 09:24:28.167 UTC 40c9cf1b-b311-466e-8d65-9f264557e1db 2012-10-17 14:46:14.892 UTC ParseYears_FromCensucensus_data11yearColumnName00censusYears1ParseYearsyear_column_name0census_data1census_years11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity census_data 1 false year_column_name 0 false census_years 1 1 false localhost 6311 false false census_data R_EXP year_column_name STRING census_years STRING_LIST net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeParseYearsyear_column_nameyearColumnNameParseYearscensus_datacensus_datacensusYearsParseYearscensus_years 1e1e8835-e729-4aba-90b7-ba7c653a1f2d 2012-07-13 07:52:51.988 UTC 0e09a93a-32f2-49b8-8a35-7499aa48b3a2 2012-07-11 12:30:56.575 UTC 8af0f726-006f-4a83-9eec-9cdc97dcb9d0 2012-07-06 06:48:37.151 UTC 8a5082d0-529b-4205-b3e2-8e0cd8812239 2012-07-06 06:56:06.356 UTC 23762bae-1b51-4606-9a3a-a2a8b04649a1 2012-07-11 13:15:24.271 UTC 81324627-fa47-4a0d-8ffe-64698a7ee09d 2012-07-13 06:24:44.255 UTC fea78a2d-fde4-46ab-9a54-741b27dc5a90 2012-07-11 12:32:16.405 UTC 450ea8ae-73da-42ed-95c9-02cf53fa556f 2012-10-17 11:43:58.240 UTC 4b236f5e-9a91-4db9-924c-6674c3231133 2012-07-13 07:50:51.370 UTC 2d4260a9-618f-45c7-9cee-2234c370a514 2012-07-11 13:14:26.564 UTC 5787d50e-f7cc-47e6-b10a-c49b57e36e27 2012-07-11 16:05:30.475 UTC cdf75737-30bb-4c7a-9693-2aa928c8569c 2012-10-17 11:46:13.979 UTC ParseYears_FromCensusData 2012-10-17 11:40:35.234 UTC Cohen_Cumulativeabundances11stage_matrix11cohenCumulative0CalculateCohenCumulativeDistanceabundances1stage_matrix1cohen_cumulative00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stage_matrix 1 false abundances 1 false cohen_cumulative 0 0 false localhost 6311 false false stage_matrix R_EXP abundances INTEGER_LIST cohen_cumulative DOUBLE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeCalculateCohenCumulativeDistanceabundancesabundancesCalculateCohenCumulativeDistancestage_matrixstage_matrixcohenCumulativeCalculateCohenCumulativeDistancecohen_cumulative 350ad4f3-8adb-42a8-bfd6-b9a67cdb739d 2013-01-22 07:43:36.673 UTC 4403aaae-f67d-4cde-8d3e-6f8f9c817f65 2013-01-22 06:45:32.736 UTC Cohen Cumulative 2013-01-22 06:45:54.433 UTC e420b581-0054-4d82-af0e-79182396dcc6 2013-01-22 06:45:58.176 UTC TransientDynamicsstage_matrix11 A stage matrix, 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. 2012-10-04 14:37:32.265 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 2012-10-04 14:37:42.656 UTC plot_title00 Here come the title of the plots. It can be the name of the species or the name of the place where the research has been conducted. 2012-10-05 07:45:27.458 UTC Gentiana pneumonanthe 2012-10-05 09:01:55.697 UTC long_term_years00 it can be any number: e.g.:50, 100, etc. 2012-10-04 14:39:24.906 UTC Number of the maximal iterations. How many iterations in the long-term will be run for the analysis. 2012-10-05 07:47:22.811 UTC short_term_years00 Number of iterations in the short-term. How many iterations in the short-term will be done for the analysis. 2012-10-04 14:33:00.156 UTC it can be any number: e.g.:5, 10, etc. 2012-10-04 14:33:31.765 UTC abundances11 The initial abundance or observed structure per stage: Example Gentiana pneumonanthe matrix stage has 5 stages with its respective abundance: stage abundance 1 S (seedlings) 69 2 J (Juveniles) 100 3 V (vegetative) 111 4 G (reproductive individuals) 21 5 D (dormant plants) 43 2012-10-09 14:11:10.929 UTC 69 100 111 21 43 2012-10-09 14:11:25.138 UTC pop_projection0 $lambda [1] 1.237596 $stable.stage S J V G D 0.14143023 0.16520742 0.65671474 0.02283244 0.01381517 $stage.vectors 0 1 2 3 4 5 6 7 8 S 69 160.9860 176.27660 188.67614 222.02436 266.74446 325.59268 400.23427 493.72709 J 100 183.9949 207.16241 222.06641 260.01317 312.04762 380.59040 467.67688 576.82384 V 111 257.9921 471.51823 694.37123 918.49323 1172.95942 1472.86815 1835.40557 2278.93197 G 21 22.9946 24.61207 28.96222 34.79578 42.47230 52.20901 64.40479 79.58338 D 43 5.9956 10.30280 14.94123 19.50731 24.78584 31.04973 38.64969 47.96428 9 10 11 12 13 14 15 16 S 610.08621 754.47932 933.40867 1154.9864 1429.2903 1768.8151 2189.0377 2709.1200 J 712.70787 881.35413 1090.35190 1349.1735 1669.5886 2066.1915 2557.0601 3164.5775 V 2824.80029 3498.56605 4331.35196 5361.3750 6635.7550 8212.7021 10164.1952 12579.2778 G 98.41890 121.75955 150.66351 186.4454 230.7351 285.5515 353.3942 437.3574 D 59.43826 73.60661 91.12249 112.7889 139.5967 172.7699 213.8226 264.6280 17 18 19 20 21 22 23 24 S 3352.7816 4149.3803 5135.2510 6355.3626 7865.3689 9734.1472 12046.940 14909.243 J 3916.4506 4846.9728 5998.5874 7423.8235 9187.6913 11370.6478 14072.266 17415.778 V 15568.1286 19267.0910 23844.8957 29510.3586 36521.9066 45199.3663 55938.553 69229.325 G 541.2706 669.8736 829.0324 1026.0069 1269.7818 1571.4766 1944.853 2406.942 D 327.5037 405.3179 501.6202 620.8033 768.3038 950.8497 1176.768 1456.363 25 26 27 28 29 30 31 32 S 18451.618 22835.646 28261.302 34976.071 43286.241 53570.874 66299.093 82051.486 J 21553.695 26674.765 33012.579 40856.232 50563.505 62577.186 77445.268 95845.945 V 85677.929 106034.653 131228.051 162407.296 200994.603 248750.094 307852.094 380996.486 G 2978.822 3686.577 4562.493 5646.522 6988.113 8648.460 10703.298 13246.358 D 1802.389 2230.629 2760.617 3416.529 4228.282 5232.904 6476.221 8014.944 33 34 35 36 37 38 39 40 S 101546.581 125673.63 155533.17 192487.21 238221.38 294821.80 364870.25 451561.92 J 118618.548 146801.83 181681.34 224848.08 278271.06 344387.11 426212.08 527478.31 V 471519.685 583550.82 722200.10 893791.88 1106153.15 1368970.59 1694232.38 2096775.03 G 16393.638 20288.70 25109.21 31075.06 38458.36 47595.91 58904.50 72899.97 D 9919.262 12276.04 15192.77 18802.51 23269.91 28798.75 35641.22 44109.42 41 42 43 44 45 46 47 48 S 558851.18 691631.92 855960.83 1059333.6 1311026.9 1622521.5 2008026.0 2485124.7 J 652804.99 807908.78 999864.58 1237428.3 1531436.2 1895299.1 2345614.4 2902922.7 V 2594960.16 3211512.02 3974554.08 4918891.8 6087600.3 7533989.1 9324033.9 11539386.0 G 90220.70 111656.77 138185.96 171018.4 211651.6 261939.2 324174.9 401197.5 D 54589.64 67559.91 83611.87 103477.7 128063.6 158491.0 196147.8 242751.7 49 S 3075580.1 J 3592645.2 V 14281096.7 G 496520.4 D 300428.5 $pop.sizes [1] 3.440000e+02 6.319632e+02 8.898721e+02 1.149017e+03 1.454834e+03 1.819010e+03 [7] 2.262310e+03 2.806371e+03 3.477031e+03 4.305452e+03 5.329766e+03 6.596899e+03 [13] 8.164769e+03 1.010497e+04 1.250603e+04 1.547751e+04 1.915496e+04 2.370613e+04 [19] 2.933864e+04 3.630939e+04 4.493635e+04 5.561305e+04 6.882649e+04 8.517938e+04 [25] 1.054177e+05 1.304645e+05 1.614623e+05 1.998250e+05 2.473027e+05 3.060607e+05 [31] 3.787795e+05 4.687760e+05 5.801552e+05 7.179977e+05 8.885910e+05 1.099717e+06 [37] 1.361005e+06 1.684374e+06 2.084574e+06 2.579860e+06 3.192825e+06 3.951427e+06 [43] 4.890269e+06 6.052177e+06 7.490150e+06 9.269779e+06 1.147224e+07 1.419800e+07 [49] 1.757138e+07 2.174627e+07 $pop.changes [1] 1.837102 1.408107 1.291216 1.266155 1.250321 1.243704 1.240489 1.238977 1.238255 [10] 1.237911 1.237746 1.237668 1.237630 1.237612 1.237604 1.237600 1.237598 1.237597 [19] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 [28] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 [37] 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 1.237596 [46] 1.237596 1.237596 1.237596 1.237596 2012-10-04 14:53:34.718 UTC Population projection The results of a matrix population analysis are a set of demographic statistics: 1) Lambda or dominant eigenvalue: The population will be stable, grow or decrease at a rate given by lambda: e.g.: λ = 1 (population is stable), λ > 1 (population is growing) and finally λ < 1 (population is decreasing). 2) The stable stage distribution: The stable population distribution is given by (w). It is the proportion of the number of individuals per stage. 3) Stage vector (stage.vectors): it is the projection of the number of individuals per stage per year in the long-term (e.g. 50 years). 4) Population sizes (pop.sizes): is the total population size in the long-term (e.g. 50 years). 5) Population change (pop.changes): are the lambda values per year in the long-term (e.g. 50 years). For further details see Caswell 2001. Caswell, H. 2001. Matrix population models: Construction, analysis and interpretation, 2nd Edition. Sinauer Associates, Sunderland, Massachusetts. 2012-10-05 08:54:10.519 UTC stage_vector_plot_short_term0 Plot the number of individuals per stage in the short-term vs years (e.g. 5, 10 years). This value is related to the short-term_years input value. 2012-10-05 07:50:04.913 UTC 2012-10-04 14:52:06.984 UTC stage_vector_plot_long_term_proportional0 Plot the proportion of individuals per stage in the short-term vs years (e.g. 5, 10 years). This value is related to the short-term_years input value. 2012-10-05 08:56:44.554 UTC stage_vector_plot_long_term_logarithmic0 Plot the number of individuals per stage in the long-term vs years (e.g. 50 years). This value is related to the long-term_years input value. 2012-10-05 07:51:23.365 UTC damping_ratio0StageVectorPlot_ShortTermiterations0plotTitle0populationProjection1stageVectorPlot00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity populationProjection 1 false plotTitle 0 false iterations 0 false stageVectorPlot 0 0 false localhost 6311 false false populationProjection R_EXP plotTitle STRING iterations INTEGER stageVectorPlot PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokePopulationProjectionabundances1years0stageMatrix1populationProjection11net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stageMatrix 1 false abundances 1 false years 0 false populationProjection 1 1 false localhost 6311 false false stageMatrix R_EXP abundances INTEGER_LIST years INTEGER populationProjection R_EXP net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeDisplayRExpressioninput1output00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.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-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeStageVectorPlot_LongTermProportionaliterations0plotTitle0populationProjection1stageVectorPlot00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity populationProjection 1 false plotTitle 0 false iterations 0 false stageVectorPlot 0 0 false localhost 6311 false false populationProjection R_EXP plotTitle STRING iterations INTEGER stageVectorPlot PNG_FILE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.ErrorBouncenet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Failovernet.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Retry 1.0 1000 5000 0 net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.InvokeDampingRatiostageMatrix1dampingRatio00net.sf.taverna.t2.activitiesrshell-activity1.5-SNAPSHOTnet.sf.taverna.t2.activities.rshell.RshellActivity stageMatrix 1 false dampingRatio 0 0 false localhost 6311 false false stageMatrix R_EXP dampingRatio DOUBLE net.sf.taverna.t2.coreworkflowmodel-impl1.5-SNAPSHOTnet.sf.taverna.t2.workflowmodel.processor.dispatch.layers.Parallelize 1 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e7224c60-2256-4a64-b82c-6b4c8712edd1 2012-12-03 14:08:40.399 UTC 85e5205a-e20f-42a5-85b6-857a1bcf6751 2012-04-27 10:04:31.201 UTC e62695c3-51aa-4a6d-8618-f111c476b87b 2012-06-08 07:13:44.794 UTC 9e035cc1-afd5-4e6e-a6aa-d2a51d92b30d 2012-10-09 13:17:10.236 UTC dcabfdeb-51a1-4ac8-91d0-f50ef43f28bb 2012-06-08 07:04:17.60 UTC Plots short-term dynamics and convergence to stage stage distribution using stage vector projections. 2012-10-04 12:48:21.328 UTC 64fd8865-03e2-4f14-a3cb-0bf43cf68a95 2012-12-09 22:46:43.753 UTC 8c458bf5-19ef-437d-a53b-a5bfa7162071 2012-06-08 07:11:39.27 UTC 93fa7d2f-2e7f-44b4-94e2-eb7f16cd96fc 2012-10-10 10:17:18.104 UTC 788d36a7-3aa5-4635-8fda-6cc6d14a7aea 2012-10-04 14:50:33.78 UTC 5ad8da23-95d6-4f62-b1fc-82689c17ffd7 2012-03-30 15:52:40.327 UTC d61ce379-a6c6-44c3-9527-a85fdb337fd9 2012-10-09 13:48:46.384 UTC 1866cddd-542f-43a6-b4a7-86d0be66758e 2012-06-27 09:47:27.817 UTC 4bf67caa-4057-482b-8fa2-201dd700c14d 2012-10-04 14:09:13.546 UTC 4363668a-7ee4-42d3-8368-ff96c8e665a9 2012-10-04 14:56:18.125 UTC 66e667a6-2f27-489f-bdd5-515c37eb3dd1 2012-10-09 13:51:04.114 UTC e2df4fe0-58a3-4d72-a606-2324a82cbea7 2012-10-04 15:01:46.375 UTC 348192ff-7a44-4a75-a3b4-af3adf25ea2b 2012-04-27 06:08:00.914 UTC 899640f6-c19b-4a69-bd65-aebd8ff84a43 2012-06-27 09:42:10.621 UTC 62b72d3b-11fe-48a9-a55a-5ccf2c9215b0 2012-10-04 14:17:26.78 UTC b4a948a5-2b99-4d2c-9a59-abe7fa51e5ef 2012-06-08 06:12:12.618 UTC 6dcdcc0c-b4b0-4951-8f57-c053e86d1173 2012-10-09 14:46:10.18 UTC 66a08a91-50e5-4bf9-a380-7b43eb4c23b1 2012-10-05 08:56:55.858 UTC This Workflow was created by: Maria Paula Balcázar-Vargas, Jonathan Giddy and G. Oostermeijer 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 The example is based on data of the article: 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. 2012-10-04 14:16:32.93 UTC